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Takei K, Inokuchi T, Hiraoka S, Ishiguro M, Toyosawa J, Aoyama Y, Igawa S, Takeuchi K, Yamasaki Y, Kinugasa H, Takahara M, Kawano S, Mitsuhashi T, Otsuka M. Efficient diagnosis for endoscopic remission in Crohn's diseases by the combination of three non-invasive markers. BMC Gastroenterol 2025; 25:364. [PMID: 40355822 PMCID: PMC12070669 DOI: 10.1186/s12876-025-03880-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Accepted: 04/10/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND Serum C-reactive protein (CRP), leucine-rich alpha-2 glycoprotein (LRG), and fecal calprotectin (Fcal) are non-invasive markers used to assess Crohn's disease (CD) severity. However, the accuracy of these markers alone is often limited, and most previous reports have evaluated the efficacy of each marker individually. We aimed to improve the diagnostic performance of endoscopic remission (ER) of CD by combining these 3 markers. METHODS We tested the diagnostic ability of various combinations of these 3 markers for endoscopic severity in 230 consecutive patients with CD from September 2014 to July 2023. The modified Simple Endoscopic Score for Crohn's disease (mSES-CD) was used to determine endoscopic severity. RESULTS Each of the 3 markers was correlated with mSED-CD (LRG: r = 0.69, CRP: r = 0.60, and Fcal: r = 0.67). A combination of 2 of the 3 markers did not increase the diagnostic accuracy of ER. However, by combining all 3 markers, the diagnostic ability for ER was improved in comparison to the diagnostic ability of the 3 individual markers, assuming that ER was obtained if 2 or 3 markers were negative. The sensitivity, specificity, and accuracy were 89%, 83%, and 86%, respectively. Additionally, we established a 2-step method using Fcal values after evaluating the 2 serum markers. This method was most useful for reducing both the patient burden and costs. CONCLUSIONS The newly established 2-step method allowed for a higher accuracy in the non-invasive diagnosis of ER when the 3 markers were combined.
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Affiliation(s)
- Kensuke Takei
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Toshihiro Inokuchi
- Research Center for Intestinal Health Science, Okayama University, Okayama, Japan
| | - Sakiko Hiraoka
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan.
| | - Mikako Ishiguro
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Junki Toyosawa
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Yuki Aoyama
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Shoko Igawa
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Keiko Takeuchi
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Yasushi Yamasaki
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Hideaki Kinugasa
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Masahiro Takahara
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Seiji Kawano
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
| | - Toshiharu Mitsuhashi
- Center for Innovative Clinical Medicine, Okayama University Hospital, Okayama, Japan
| | - Motoyuki Otsuka
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2 - 5- 1 Shikata-Cho, Kita-Ku, Okayama, 700 - 8558, Japan
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Dalky A, Altawalbih M, Alshanik F, Khasawneh RA, Tawalbeh R, Al-Dekah AM, Alrawashdeh A, Quran TO, ALBashtawy M. Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis. Healthcare (Basel) 2025; 13:892. [PMID: 40281841 PMCID: PMC12026717 DOI: 10.3390/healthcare13080892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2025] [Revised: 04/02/2025] [Accepted: 04/08/2025] [Indexed: 04/29/2025] Open
Abstract
Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer's disease, Parkinson's diseases, COVID-19, and diabetes. The author keyword analysis identified "deep learning", "convolutional neural network", and "classification" as dominant research themes. Conclusions: AI's transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.
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Affiliation(s)
- Alaa Dalky
- Department of Health Management and Policy, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Mahmoud Altawalbih
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan; (M.A.); (R.T.); (A.A.)
| | - Farah Alshanik
- Department of Computer Science, Faculty of Computer & Information Technology, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Rawand A. Khasawneh
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Rawan Tawalbeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan; (M.A.); (R.T.); (A.A.)
| | - Arwa M. Al-Dekah
- Department of Biotechnology and Genetic Engineering, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Ahmad Alrawashdeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan; (M.A.); (R.T.); (A.A.)
| | - Tamara O. Quran
- Department of Health Management and Policy, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Mohammed ALBashtawy
- Department of Community and Mental Health Nursing, Princess Salma Faculty of Nursing, Al al-Bayt University, Mafraq 25113, Jordan;
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Holt NM, Byrne MF. The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease. Gastrointest Endosc Clin N Am 2025; 35:291-308. [PMID: 40021230 DOI: 10.1016/j.giec.2024.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
Artificial intelligence (AI) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel.
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Affiliation(s)
- Nicholas Mathew Holt
- Gastroenterology and Hepatology Unit, The Canberra Hospital, Yamba Drive, Garran, ACT 2605, Australia.
| | - Michael Francis Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, UBC Division of Gastroenterology, 5153 - 2775 Laurel Street, Vancouver, British Columbia V5Z 1M9, Canada
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Cheng M, Zhang H, Guo Y, Lyu P, Yan J, Liu Y, Liang P, Ren Z, Gao J. Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma. Sci Rep 2025; 15:9629. [PMID: 40113926 PMCID: PMC11926170 DOI: 10.1038/s41598-025-92263-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2024] [Accepted: 02/26/2025] [Indexed: 03/22/2025] Open
Abstract
Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological characteristics. The objective of this study was to evaluate the efficacy of deep learning (DL) radiomics analysis, performed using computed tomography (CT) and magnetic resonance imaging (MRI), in diagnosing iCCA within PLC. 178 pathologically confirmed PLC patients (training cohort: test cohort = 124: 54) who underwent both CT and MRI examinations was enrolled. Univariate and multivariate analysis was used to identify the significant factors of radiological findings for diagnosing iCCA. DL radiomics analysis was applied to CT and MRI images, respectively. We constructed and evaluated six distinct models: CT DL radiomics (DLRSCT), CT radiological (RCT), CT DL radiomics-radiological (DLRRCT), MRI DL radiomics (DLRSMRI), MRI radiological (RMRI) and MRI DL radiomics-radiological (DLRRMRI). To further explore the diagnostic and predictive value of a cross-modal approach, we developed a fused model that combined DLRRCT and DLRRMRI. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were employed to compare the performance of different models. MRI-based models demonstrated a superior predictive performance than CT-based models in test cohort (AUCs of MRI vs. CT: DLRR, 0.923 vs. 0.880, P = 0.521; DLRS, 0.875 vs. 0.867, P = 0.922; R, 0.859 vs. 0.840, P = 0.808). The CT-MRI cross-modal model yielded the highest AUC of 0.994 and 0.937 in training and test cohorts, respectively. CT- and MRI-based DL radiomics analyses exhibited good performance in diagnosing iCCA, and the CT-MRI cross-modal model may have significant clinical implications on detection of liver malignancies.
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Affiliation(s)
- Ming Cheng
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Hanyue Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yimin Guo
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Peijie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yin Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhigang Ren
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
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Khorsand B, Rajabnia M, Jahanian A, Fathy M, Taghvaei S, Houri H. Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data. Adv Med Sci 2025; 70:1-7. [PMID: 39419440 DOI: 10.1016/j.advms.2024.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/07/2024] [Accepted: 10/14/2024] [Indexed: 10/19/2024]
Abstract
PURPOSE Spontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortality rate of 40 %, accurate diagnosis and prompt initiation of appropriate antibiotic therapy are crucial for optimizing patient outcomes and preventing life-threatening complications. This study aimed to expand the use of computational models to improve the diagnostic accuracy of SBP in cirrhotic patients by incorporating a broader range of data, including clinical variables and laboratory values. PATIENTS AND METHODS We employed 5 machine learning classification methods - Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Random Forest, utilizing a variety of demographic, clinical, and laboratory features and biomarkers. RESULTS Ascitic fluid markers, including white blood cell (WBC) count, lactate dehydrogenase (LDH), total protein, and polymorphonuclear cells (PMN), significantly differentiated between SBP and non-SBP patients. The Random Forest model demonstrated the highest overall accuracy at 86 %, while the Naive Bayes model achieved the highest sensitivity at 72 %. Utilizing 10 key features instead of the full feature set improved model performance, notably enhancing specificity and accuracy. CONCLUSION Our analysis highlights the potential of machine learning to enhance the accuracy of SBP diagnosis in cirrhotic patients. Integrating these models into clinical workflows could substantially improve patient outcomes. To achieve this, ongoing multidisciplinary research is crucial. Ensuring model interpretability, continuous monitoring, and rigorous validation will be essential for the successful implementation of real-time clinical decision support systems.
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Affiliation(s)
- Babak Khorsand
- Department of Neurology, University of California, Irvine, CA, USA
| | - Mohsen Rajabnia
- Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran.
| | - Ali Jahanian
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mobin Fathy
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Somayye Taghvaei
- Department of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Hamidreza Houri
- Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Ovi TB, Bashree N, Nyeem H, Wahed MA. FocusU 2Net: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation. Comput Biol Med 2025; 186:109617. [PMID: 39793349 DOI: 10.1016/j.compbiomed.2024.109617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 12/09/2024] [Accepted: 12/22/2024] [Indexed: 01/13/2025]
Abstract
The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images expedites diagnosis and aids in the precise identification of adenomatous polyps, thus mitigating the burden of manual image analysis. This study introduces FocusU2Net, an innovative bi-level nested U-structure integrated with a dual-attention mechanism. The model integrates Focus Gate (FG) modules for spatial and channel-wise attention and Residual U-blocks (RSU) with multi-scale receptive fields for capturing diverse contextual information. Comprehensive evaluations on five benchmark datasets - Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETISLarib, and EndoScene - demonstrate Dice score improvements of 3.14% to 43.59% over state-of-the-art models, with an 85% success rate in cross-dataset validations, significantly surpassing prior competing models with sub-5% success rates. The model combines high segmentation accuracy with computational efficiency, featuring 46.64 million parameters, 78.09 GFLOPs, and 39.02 GMacs, making it suitable for real-time applications. Enhanced with Explainable AI techniques, FocusU2Net provides clear insights into its decision-making process, improving interpretability. This combination of high performance, efficiency, and transparency positions FocusU2Net as a powerful, scalable solution for automated polyp segmentation in clinical practice, advancing medical image analysis and computer-aided diagnosis.
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Affiliation(s)
- Tareque Bashar Ovi
- Department of EECE, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh.
| | - Nomaiya Bashree
- Department of EECE, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh.
| | - Hussain Nyeem
- Department of EECE, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh.
| | - Md Abdul Wahed
- Department of EECE, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh.
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Boros E, Pintér J, Molontay R, Prószéky KG, Vörhendi N, Simon OA, Teutsch B, Pálinkás D, Frim L, Tari E, Gagyi EB, Szabó I, Hágendorn R, Vincze Á, Izbéki F, Abonyi-Tóth Z, Szentesi A, Vass V, Hegyi P, Erőss B. New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding. Sci Rep 2025; 15:6371. [PMID: 39984590 PMCID: PMC11845789 DOI: 10.1038/s41598-025-90986-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Accepted: 02/17/2025] [Indexed: 02/23/2025] Open
Abstract
Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyzed the prospective, multicenter Hungarian GIB Registry's data. The predictive performance of XGBoost and CatBoost machine-learning algorithms with the Glasgow-Blatchford (GBS), pre-endoscopic Rockall and ABC scores were compared. We evaluated our models using five-fold cross-validation, and performance was measured by area under receiver operating characteristic curve (AUC) analysis with 95% confidence intervals (CI). Overall, we included 1,021 patients in the analysis. In-hospital death occurred in 108 cases. The XGBoost and the CatBoost model identified patients who died with an AUC of 0.84 (CI:0.76-0.90; 0.77-0.90; respectively) in the internal validation set, whereas the GBS and pre-endoscopic Rockall clinical scoring system's performance was significantly lower, AUC values of 0.68 (CI:0.62-0.74) and 0.62 (CI:0.56-0.67), respectively. ABC score had an AUC of 0.77 (CI:0.71-0.83). The XGBoost model had a specificity of 0.96 (CI:0.92-0.98) at a sensitivity of 0.25 (CI:0.10-0.43) compared with the CatBoost model, which had a specificity of 0.74 (CI:0.66-0.83) at a sensitivity of 0.78 (CI:0.57-0.95). XGBoost and the CatBoost models evaluate the mortality risk of acute GI bleeding better, than the conventional risk assessment tools.
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Affiliation(s)
- Eszter Boros
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Fejér County Szent György University Teaching Hospital, Székesfehérvár, Hungary
| | - József Pintér
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Roland Molontay
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
- Institute of Biostatistics and Network Science, Semmelweis University, Budapest, Hungary
| | - Kristóf Gergely Prószéky
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Nóra Vörhendi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Internal Medicine, Hospital and Clinics of Siófok, Siófok, Hungary
| | - Orsolya Anna Simon
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- First Department of Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Brigitta Teutsch
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Dániel Pálinkás
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
- Department of Gastroenterology, Central Hospital of Northern Pest - Military Hospital, Budapest, Hungary
| | - Levente Frim
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Edina Tari
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
- Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungry, Hungary
| | - Endre Botond Gagyi
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
- Selye János Doctoral College for Advanced Studies, Semmelweis University, Budapest, Hungary
| | - Imre Szabó
- First Department of Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Roland Hágendorn
- First Department of Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Áron Vincze
- First Department of Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Ferenc Izbéki
- Fejér County Szent György University Teaching Hospital, Székesfehérvár, Hungary
| | - Zsolt Abonyi-Tóth
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
- Department of Biostatistics, University of Veterinary Medicine, Budapest, Hungary
| | - Andrea Szentesi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Vivien Vass
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Péter Hegyi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Bálint Erőss
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary.
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary.
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Prada AG, Stroie T, Diculescu RI, Gogîrlă GC, Radu CD, Istratescu D, Prada GI, Diculescu MM. Artificial Intelligence as a Tool in Diagnosing Inflammatory Bowel Disease in Older Adults. J Clin Med 2025; 14:1360. [PMID: 40004890 PMCID: PMC11856854 DOI: 10.3390/jcm14041360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2025] [Revised: 02/10/2025] [Accepted: 02/15/2025] [Indexed: 02/27/2025] Open
Abstract
Background/Objectives: The primary objective of our study was to find a potential use for images generated by imagistic investigations by comparing the appearance of a healthy digestive tract to that of a pathological one. Methods: We conducted a cross-sectional observational study involving 60 older adult patients admitted to and followed up at a primary center in Romania. Our focus was on different diagnostic methods and the use of artificial intelligence (AI) tools integrated into the electronic health records system. Results: Currently, imagery, laboratory values and electronic health records (EHR) can also be used to train AI models. Comparative imagery to predict the appearance of inflammatory bowel disease (IBD) can be used as a predictor model. Conclusions: Our findings indicate with certainty that training a tool in the diagnosis and prevention of relapses in older adults with IBD is promising for further integrating these models into patient care.
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Affiliation(s)
- Ana-Gabriela Prada
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
| | - Tudor Stroie
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - Rucsandra-Ilinca Diculescu
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - George Cristian Gogîrlă
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - Codruța Delia Radu
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
| | - Doina Istratescu
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
| | - Gabriel Ioan Prada
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Naţional de Gerontologie și Geriatrie “Ana Aslan” Bucuresti (“Ana Aslan” National Institute of Gerontology and Geriatrics), Bucharest 011241, Romania
| | - Mihai Mircea Diculescu
- Faculty of Medicine, “Carol Davila” University of Medicine and Pharmacy Bucharest, Bucharest 050474, Romania; (A.-G.P.); (R.-I.D.); (D.I.); (M.M.D.)
- Institutul Clinic FUNDENI Bucuresti (Fundeni Clinical Institute Bucharest), Bucharest 077086, Romania; (G.C.G.); (C.D.R.)
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Tawheed A, Ismail A, Amer MS, Elnahas O, Mowafy T. Capsule endoscopy: Do we still need it after 24 years of clinical use? World J Gastroenterol 2025; 31:102692. [PMID: 39926220 PMCID: PMC11718605 DOI: 10.3748/wjg.v31.i5.102692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 11/20/2024] [Accepted: 12/02/2024] [Indexed: 12/30/2024] Open
Abstract
In this letter, we comment on a recent article published in the World Journal of Gastroenterology by Xiao et al, where the authors aimed to use a deep learning model to automatically detect gastrointestinal lesions during capsule endoscopy (CE). CE was first presented in 2000 and was approved by the Food and Drug Administration in 2001. The indications of CE overlap with those of regular diagnostic endoscopy. However, in clinical practice, CE is usually used to detect lesions in areas inaccessible to standard endoscopies or in cases of bleeding that might be missed during conventional endoscopy. Since the emergence of CE, many physiological and technical challenges have been faced and addressed. In this letter, we summarize the current challenges and briefly mention the proposed methods to overcome these challenges to answer a central question: Do we still need CE?
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Affiliation(s)
- Ahmed Tawheed
- Department of Endemic Medicine, Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Alaa Ismail
- Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Mohab S Amer
- Faculty of Medicine, Helwan University, Cairo 11795, Egypt
- Department of Research, SMART Company for Research Services, Cairo 11795, Egypt
| | - Osama Elnahas
- Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Tawhid Mowafy
- Department of Internal Medicine, Gardenia Medical Center, Doha 0000, Qatar
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Alqahtani SA, AlAhmed RS, AlOmaim WS, Alghamdi S, Al-Hamoudi W, Bzeizi KI, Albenmousa A, Aghemo A, Pugliese N, Hassan C, Abaalkhail FA. Assessment of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction-associated steatotic liver disease. PLoS One 2025; 20:e0317929. [PMID: 39899495 PMCID: PMC11790096 DOI: 10.1371/journal.pone.0317929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Accepted: 01/07/2025] [Indexed: 02/05/2025] Open
Abstract
BACKGROUND AND AIM Artificial intelligence (AI)-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have shown promising results in healthcare settings. These tools can help patients obtain real-time responses to queries, ensuring immediate access to relevant information. The study aimed to explore the potential use of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS An English patient questionnaire on MASLD was translated to Arabic. The Arabic questions were then entered into ChatGPT 3.5 on November 12, 2023. The responses were evaluated for accuracy, completeness, and comprehensibility by 10 Saudi MASLD experts who were native Arabic speakers. Likert scales were used to evaluate: 1) Accuracy, 2) Completeness, and 3) Comprehensibility. The questions were grouped into 3 domains: (1) Specialist referral, (2) Lifestyle, and (3) Physical activity. RESULTS Accuracy mean score was 4.9 ± 0.94 on a 6-point Likert scale corresponding to "Nearly all correct." Kendall's coefficient of concordance (KCC) ranged from 0.025 to 0.649, with a mean of 0.28, indicating moderate agreement between all 10 experts. Mean completeness score was 2.4 ± 0.53 on a 3-point Likert scale corresponding to "Comprehensive" (KCC: 0.03-0.553; mean: 0.22). Comprehensibility mean score was 2.74 ± 0.52 on a 3-point Likert scale, which indicates the responses were "Easy to understand" (KCC: 0.00-0.447; mean: 0.25). CONCLUSION MASLD experts found that ChatGPT responses were accurate, complete, and comprehensible. The results support the increasing trend of leveraging the power of AI chatbots to revolutionize the dissemination of information for patients with MASLD. However, many AI-powered chatbots require further enhancement of scientific content to avoid the risks of circulating medical misinformation.
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Affiliation(s)
- Saleh A. Alqahtani
- Liver, Digestive, and Lifestyle Health Research Section, and Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
- Division of Gastroenterology and Hepatology, Weill Cornell Medicine, New York, New York, United States of America
| | - Reem S. AlAhmed
- Liver, Digestive, and Lifestyle Health Research Section, and Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Waleed S. AlOmaim
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Saad Alghamdi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Waleed Al-Hamoudi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Khalid Ibrahim Bzeizi
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Ali Albenmousa
- Organ Transplant Center of Excellence, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Alessio Aghemo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (MI), Italy
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy
| | - Nicola Pugliese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (MI), Italy
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (MI), Italy
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy
| | - Faisal A. Abaalkhail
- Gastroenterology Section, Department of Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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11
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Cai Y, Chen X, Chen J, Liao J, Han M, Lin D, Hong X, Hu H, Hu J. Deep learning-assisted colonoscopy images for prediction of mismatch repair deficiency in colorectal cancer. Surg Endosc 2025; 39:859-867. [PMID: 39623175 DOI: 10.1007/s00464-024-11426-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Accepted: 11/12/2024] [Indexed: 02/06/2025]
Abstract
BACKGROUND Deficient mismatch repair or microsatellite instability is a major predictive biomarker for the efficacy of immune checkpoint inhibitors of colorectal cancer. However, routine testing has not been uniformly implemented due to cost and resource constraints. METHODS We developed and validated a deep learning-based classifiers to detect mismatch repair-deficient status from routine colonoscopy images. We obtained the colonoscopy images from the imaging database at Endoscopic Center of the Sixth Affiliated Hospital, Sun Yat-sen University. Colonoscopy images from a prospective trial (Neoadjuvant PD-1 blockade by toripalimab with or without celecoxib in mismatch repair-deficient or microsatellite instability-high locally advanced colorectal cancer) were used to test the model. RESULTS A total of 5226 eligible images from 892 tumors from the consecutive patients were utilized to develop and validate the deep learning model. 2105 colorectal cancer images from 306 tumors were randomly selected to form model development dataset with a class-balanced approach. 3121 images of 488 proficient mismatch repair tumors and 98 deficient mismatch repair tumors were used to form the independent dataset. The model achieved an AUROC of 0.948 (95% CI 0.919-0.977) on the test dataset. On the independent validation dataset, the AUROC was 0.807 (0.760-0.854), and the NPV in was 94.2% (95% CI 0.918-0.967). On the prospective trial dataset, the model identified 29 tumors among the 33 deficient mismatch repair tumors (87.88%). CONCLUSIONS The model achieved a high NPV in detecting deficient mismatch repair colorectal cancers. This model might serve as an automatic screening tool.
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Affiliation(s)
- Yue Cai
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
| | - Xijie Chen
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of General Surgery (Gastric Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Junguo Chen
- Department of Thoracic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - James Liao
- Guangzhou Aptiligent Technology Co. Ltd., Guangzhou, Guangdong, China
| | - Ming Han
- Guangzhou Aptiligent Technology Co. Ltd., Guangzhou, Guangdong, China
| | - Dezheng Lin
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of General Surgery (Endoscopic Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoling Hong
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of General Surgery (Endoscopic Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Huabin Hu
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China.
| | - Jiancong Hu
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, Guangdong, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
- Department of General Surgery (Endoscopic Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
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de Oliveira MBM, Mendes F, Martins M, Cardoso P, Fonseca J, Mascarenhas T, Saraiva MM. The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects. Diagnostics (Basel) 2025; 15:274. [PMID: 39941204 PMCID: PMC11816405 DOI: 10.3390/diagnostics15030274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2024] [Revised: 01/09/2025] [Accepted: 01/17/2025] [Indexed: 02/16/2025] Open
Abstract
Artificial intelligence (AI) is the new medical hot topic, being applied mainly in specialties with a strong imaging component. In the domain of gynecology, AI has been tested and shown vast potential in several areas with promising results, with an emphasis on oncology. However, fewer studies have been made focusing on urogynecology, a branch of gynecology known for using multiple imaging exams (IEs) and tests in the management of women's pelvic floor health. This review aims to illustrate the current state of AI in urogynecology, namely with the use of machine learning (ML) and deep learning (DL) in diagnostics and as imaging tools, discuss possible future prospects for AI in this field, and go over its limitations that challenge its safe implementation.
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Affiliation(s)
- Maria Beatriz Macedo de Oliveira
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.M.d.O.); (P.C.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.M.d.O.); (P.C.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - João Fonseca
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal;
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.M.d.O.); (P.C.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.M.d.O.); (P.C.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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13
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Saraiva MM, Ribeiro T, Agudo B, Afonso J, Mendes F, Martins M, Cardoso P, Mota J, Almeida MJ, Costa A, Gonzalez Haba Ruiz M, Widmer J, Moura E, Javed A, Manzione T, Nadal S, Barroso LF, de Parades V, Ferreira J, Macedo G. Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology. J Clin Med 2025; 14:572. [PMID: 39860582 PMCID: PMC11765803 DOI: 10.3390/jcm14020572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2024] [Revised: 12/15/2024] [Accepted: 12/30/2024] [Indexed: 01/27/2025] Open
Abstract
Background: Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4's performance in interpreting gastroenterology images. Methods: A total of 740 images from five procedures-capsule endoscopy (CE), device-assisted enteroscopy (DAE), endoscopic ultrasound (EUS), digital single-operator cholangioscopy (DSOC), and high-resolution anoscopy (HRA)-were included and analyzed by ChatGPT-4 using a predefined prompt for each. ChatGPT-4 predictions were compared to gold standard diagnoses. Statistical analyses included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Results: For CE, ChatGPT-4 demonstrated accuracies ranging from 50.0% to 90.0%, with AUCs of 0.50-0.90. For DAE, the model demonstrated an accuracy of 67.0% (AUC 0.670). For EUS, the system showed AUCs of 0.488 and 0.550 for the differentiation between pancreatic cystic and solid lesions, respectively. The LLM differentiated benign from malignant biliary strictures with an AUC of 0.550. For HRA, ChatGPT-4 showed an overall accuracy between 47.5% and 67.5%. Conclusions: ChatGPT-4 demonstrated suboptimal diagnostic accuracies for image interpretation across several gastroenterology techniques, highlighting the need for continuous improvement before clinical adoption.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Belén Agudo
- Department of Gastroenterology, Hospital Universitario Puerta de Hierro Majadahonda, C/Joaquín Rodrigo, 28220 Madrid, Spain; (B.A.); (A.C.); (M.G.H.R.)
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria Joao Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - António Costa
- Department of Gastroenterology, Hospital Universitario Puerta de Hierro Majadahonda, C/Joaquín Rodrigo, 28220 Madrid, Spain; (B.A.); (A.C.); (M.G.H.R.)
| | - Mariano Gonzalez Haba Ruiz
- Department of Gastroenterology, Hospital Universitario Puerta de Hierro Majadahonda, C/Joaquín Rodrigo, 28220 Madrid, Spain; (B.A.); (A.C.); (M.G.H.R.)
| | - Jessica Widmer
- Division of Gastroenterology, NYU Langone Hospital—Long Island, 259 First Street Mineola, New York, NY 11501, USA;
| | - Eduardo Moura
- Department of Gastrointestinal Endoscopy, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Rua Dr. Ovídio Pires de Campos 225, Sao Paulo 05403-010, Brazil;
| | - Ahsan Javed
- Department of Colorectal Surgery, Royal Liverpool University Hospital, Liverpool L7 8YE, UK;
| | - Thiago Manzione
- Department of Surgery, Instituto de Infectologia Emílio Ribas, São Paulo 01246-900, Brazil; (T.M.); (S.N.)
| | - Sidney Nadal
- Department of Surgery, Instituto de Infectologia Emílio Ribas, São Paulo 01246-900, Brazil; (T.M.); (S.N.)
| | - Luis F. Barroso
- Internal Medicine/Infectious Diseases, Wake Forest University Health Sciences, Winston-Salem, NC 27109, USA;
| | - Vincent de Parades
- Department of Proctology, Hôpital Paris Saint-Joseph, 85, Rue Raymond Losserand, 75014 Paris, France;
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (F.M.); (M.M.); (P.C.); (J.M.); (M.J.A.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Zhou Y, Liu RD, Gong H, Yuan XL, Hu B, Huang ZY. Multimodal artificial intelligence system for detecting a small esophageal high-grade squamous intraepithelial neoplasia: A case report. World J Gastrointest Endosc 2025; 17:101233. [PMID: 39850915 PMCID: PMC11752473 DOI: 10.4253/wjge.v17.i1.101233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 11/21/2024] [Accepted: 12/06/2024] [Indexed: 01/16/2025] Open
Abstract
BACKGROUND Recent advancements in artificial intelligence (AI) have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases. AI has shown great promise in clinical practice, particularly for diagnostic support, offering real-time insights into complex conditions such as esophageal squamous cell carcinoma. CASE SUMMARY In this study, we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy, highlighting its potential for early detection of malignancies. The lesion was confirmed as high-grade squamous intraepithelial neoplasia, with pathology results supporting the AI system's accuracy. The multimodal AI system offers an integrated solution that provides real-time, accurate diagnostic information directly within the endoscopic device interface, allowing for single-monitor use without disrupting endoscopist's workflow. CONCLUSION This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier, more accurate interventions.
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Affiliation(s)
- Yang Zhou
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Rui-De Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Gong
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xiang-Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, Medical Engineering Integration Laboratory of Digestive Endoscopy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zhi-Yin Huang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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15
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Zeng S, Dong C, Liu C, Zhen J, Pu Y, Hu J, Dong W. The global research of artificial intelligence on inflammatory bowel disease: A bibliometric analysis. Digit Health 2025; 11:20552076251326217. [PMID: 40093709 PMCID: PMC11909680 DOI: 10.1177/20552076251326217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Accepted: 02/18/2025] [Indexed: 03/19/2025] Open
Abstract
Aims This study aimed to evaluate the related research on artificial intelligence (AI) in inflammatory bowel disease (IBD) through bibliometrics analysis and identified the research basis, current hotspots, and future development. Methods The related literature was acquired from the Web of Science Core Collection (WoSCC) on 31 December 2024. Co-occurrence and cooperation relationship analysis of (cited) authors, institutions, countries, cited journals, references, and keywords in the literature were carried out through CiteSpace 6.1.R6 software and the Online Analysis platform of Literature Metrology. Meanwhile, relevant knowledge maps were drawn, and keywords clustering analysis was performed. Results According to WoSCC, 1919 authors, 790 research institutions, 184 journals, and 49 countries/regions published 176 AI-related papers in IBD during 1999-2024. The number of papers published has increased significantly since 2019, reaching a maximum by 2023. The United States had the highest number of publications and the closest collaboration with other countries. The clustering analysis showed that the earliest studies focused on "psychometric value" and then moved to "deep learning model," "intestinal ultrasound," and "new diagnostic strategies." Conclusion This study is the first bibliometric analysis to summarize the current status and to visually reveal the development trends and future research hotspots of the application of AI in IBD. The application of AI in IBD is still in its infancy, and the focus of this field will shift to improving the efficiency of diagnosis and treatment through deep learning techniques, big data-based treatment, and prognosis prediction.
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Affiliation(s)
- Suqi Zeng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chenyu Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chuan Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Junhai Zhen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yu Pu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jiaming Hu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Weiguo Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Glicksman M, Wang S, Yellapragada S, Robinson C, Orhurhu V, Emerick T. Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee. Pain Pract 2025; 25:e13428. [PMID: 39588809 DOI: 10.1111/papr.13428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there is a paucity of literature analyzing the impact that AI may have on trainee education. As such, we sought to assess the benefits and pitfalls that AI may have on pain medicine trainee education. Given the rapidly increasing popularity of LLMs, we particularly assessed how these LLMs may promote and hinder trainee education through a pilot quality improvement project. MATERIALS AND METHODS A comprehensive search of the existing literature regarding AI within medicine was performed to identify its potential benefits and pitfalls within pain medicine. The pilot project was approved by UPMC Quality Improvement Review Committee (#4547). Three of the most commonly utilized LLMs at the initiation of this pilot study - ChatGPT Plus, Google Bard, and Bing AI - were asked a series of multiple choice questions to evaluate their ability to assist in learner education within pain medicine. RESULTS Potential benefits of AI within pain medicine trainee education include ease of use, imaging interpretation, procedural/surgical skills training, learner assessment, personalized learning experiences, ability to summarize vast amounts of knowledge, and preparation for the future of pain medicine. Potential pitfalls include discrepancies between AI devices and associated cost-differences, correlating radiographic findings to clinical significance, interpersonal/communication skills, educational disparities, bias/plagiarism/cheating concerns, lack of incorporation of private domain literature, and absence of training specifically for pain medicine education. Regarding the quality improvement project, ChatGPT Plus answered the highest percentage of all questions correctly (16/17). Lowest correctness scores by LLMs were in answering first-order questions, with Google Bard and Bing AI answering 4/9 and 3/9 first-order questions correctly, respectively. Qualitative evaluation of these LLM-provided explanations in answering second- and third-order questions revealed some reasoning inconsistencies (e.g., providing flawed information in selecting the correct answer). CONCLUSIONS AI represents a continually evolving and promising modality to assist trainees pursuing a career in pain medicine. Still, limitations currently exist that may hinder their independent use in this setting. Future research exploring how AI may overcome these challenges is thus required. Until then, AI should be utilized as supplementary tool within pain medicine trainee education and with caution.
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Affiliation(s)
- Michael Glicksman
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania, USA
| | - Sheri Wang
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania, USA
| | - Samir Yellapragada
- University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Christopher Robinson
- Department of Anesthesiology, Perioperative, and Pain Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Vwaire Orhurhu
- University of Pittsburgh Medical Center (UPMC), Susquehanna, Williamsport, Pennsylvania, USA
- MVM Health, East Stroudsburg, Pennsylvania, USA
| | - Trent Emerick
- Department of Anesthesiology and Perioperative Medicine, Chronic Pain Division, University of Pittsburgh Medical Center (UPMC), Pittsburgh, Pennsylvania, USA
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Wang F, Liu X, Hao X, Wang J, Liu J, Bai C. Oviduct Glycoprotein 1 (OVGP1) Diagnoses Polycystic Ovary Syndrome (PCOS) Based on Machine Learning Algorithms. ACS OMEGA 2024; 9:49054-49063. [PMID: 39713694 PMCID: PMC11656370 DOI: 10.1021/acsomega.4c03111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 11/10/2024] [Accepted: 11/18/2024] [Indexed: 12/24/2024]
Abstract
Aims: To investigate the diagnostic value of oviduct glycoprotein 1 (OVGP1) levels for polycystic ovary syndrome (PCOS). Materials and Methods: Serum OVGP1 concentrations were measured by enzyme-linked immunosorbent assay (ELISA). Associations between OVGP1 and endocrine parameters were evaluated by Spearman's correlation analysis. Diagnostic capacity was assessed by utilizing machine learning algorithms and receiver operating characteristic (ROC) curves. Results: OVGP1 levels were significantly decreased in PCOS patients and correlated with the serum follicle-stimulating hormone (FSH) concentration and the luteinizing hormone/follicle-stimulating hormone (LH/FSH) ratio, which are predictors of PCOS occurrence. The diagnostic value of OVGP1 combined with six signatures (LH/FSH, progesterone, total cholesterol, triglyceride, high-density lipoprotein cholesterol, and anti-Müllerian hormone) or three clinical indicators has the potential to significantly improve the accuracy of diagnosing PCOS patients. Conclusion: OVGP1 enhances the ability to diagnose when combined with clinical indicators.
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Affiliation(s)
| | | | - Xiaoyan Hao
- Department of Clinical Laboratory
Medicine, Xijing Hospital, Fourth Military
Medical University (Air Force Military Medical University), Xi’an 710032, China
| | - Jing Wang
- Department of Clinical Laboratory
Medicine, Xijing Hospital, Fourth Military
Medical University (Air Force Military Medical University), Xi’an 710032, China
| | - Jiayun Liu
- Department of Clinical Laboratory
Medicine, Xijing Hospital, Fourth Military
Medical University (Air Force Military Medical University), Xi’an 710032, China
| | - Congxia Bai
- Department of Clinical Laboratory
Medicine, Xijing Hospital, Fourth Military
Medical University (Air Force Military Medical University), Xi’an 710032, China
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Yuan P, Ma ZH, Yan Y, Li SJ, Wang J, Wu Q. Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images. Int J Gen Med 2024; 17:6127-6138. [PMID: 39691834 PMCID: PMC11649499 DOI: 10.2147/ijgm.s481127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 06/03/2024] [Indexed: 12/19/2024] Open
Abstract
Background A full examination of gastrointestinal tract is an essential prerequisite for effectively detecting gastrointestinal lesions. However, there is a lack of efficient tools to analyze and recognize gastric anatomy locations, preventing the complete portrayal of entire stomach. This study aimed to evaluate the effectiveness of artificial intelligence in identifying gastric anatomy sites by analyzing esophagogastroduodenoscopy images. Methods Using endoscopic images, we proposed a system called the Artificial Intelligence of Medicine (AIMED) through convolutional neural networks and MobileNetV3-large. The performance of artificial intelligence in the recognition of anatomic sites in esophagogastroduodenoscopy images was evaluated by considering many cases. Primary outcomes included diagnostic accuracy, sensitivity, and specificity. Results A total of 160,308 images from 27 categories of the upper endoscopy anatomy classification were included in this retrospective research. As a test group, 16031 esophagogastroduodenoscopy images with 27 categories were used to evaluate AIMED's performance in identifying gastric anatomy sites. The convolutional neural network's accuracy, sensitivity, and specificity were determined to be 99.40%, 91.85%, and 99.69%, respectively. Conclusion The AIMED system achieved high accuracy with regard to recognizing gastric anatomy sites, and it could assist the operator in enhancing the quality control of the used endoscope. Moreover, it could contribute to a more standardized endoscopic performance. Overall, our findings prove that artificial-intelligence-based systems can be indispensable to the endoscopic revolution (Clinical trial registration number: NCT04384575 (12/05/2020)).
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Affiliation(s)
- Peng Yuan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Zhong-Hua Ma
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Yan Yan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Shi-Jie Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Jing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
| | - Qi Wu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Endoscopy, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China
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Lee H, Chung JW, Kim KO, Kwon KA, Kim JH, Yun SC, Jung SW, Sheeraz A, Yoon YJ, Kim JH, Kayasseh MA. Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy. Diagnostics (Basel) 2024; 14:2762. [PMID: 39682670 DOI: 10.3390/diagnostics14232762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 12/02/2024] [Accepted: 12/04/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND/OBJECTIVES Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. METHODS We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®. RESULTS The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®. CONCLUSIONS The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance.
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Affiliation(s)
- Hannah Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Kyoung Oh Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Kwang An Kwon
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Jung Ho Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea
| | - Sung-Cheol Yun
- Division of Biostatistics, Center for Medical Research and Information, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
| | - Sung Woo Jung
- Division of Gastroenterology, Department of Internal Medicine, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | | | | | - Ji Hee Kim
- CAIMI Co., Ltd., Incheon 22004, Republic of Korea
| | - Mohd Azzam Kayasseh
- Division of Gastroenterology, Dr. Sulaiman AI Habib Medical Group, Dubai Healthcare City, Dubai 51431, United Arab Emirates
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Wei X, Xi P, Chen M, Wen Y, Wu H, Wang L, Zhu Y, Ren Y, Gu Z. Capsule robots for the monitoring, diagnosis, and treatment of intestinal diseases. Mater Today Bio 2024; 29:101294. [PMID: 39483392 PMCID: PMC11525164 DOI: 10.1016/j.mtbio.2024.101294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 09/21/2024] [Accepted: 10/06/2024] [Indexed: 11/03/2024] Open
Abstract
Current evidence suggests that the intestine as the new frontier for human health directly impacts both our physical and mental health. Therefore, it is highly desirable to develop the intelligent tool for the enhanced diagnosis and treatment of intestinal diseases. During the past 20 years, capsule robots have opened new avenues for research and clinical applications, potentially revolutionizing human health monitor, disease diagnosis and treatment. In this review, we summarize the research progress of edible multifunctional capsule robots in intestinal diseases. To begin, we introduce the correlation between the intestinal microbiome, intestinal gas and human diseases. After that, we focus on the technical structure of edible multifunctional robots. Subsequently, the biomedical applications in the monitoring, diagnosis and treatment of intestinal diseases are discussed in detail. Last but not least, the main challenges of multifunctional capsule robots during the development process are summarized, followed by a vision for future development opportunities.
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Affiliation(s)
- Xiangyu Wei
- Department of Rheumatology, Research Center of Immunology, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
- Department of Rheumatology, Affiliated Municipal Hospital of Xuzhou Medical University, Xuzhou, 221100, China
- Suzhou Medical College, Soochow University, Suzhou, 215123, China
| | - Peipei Xi
- Department of Emergency, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
- Suzhou Medical College, Soochow University, Suzhou, 215123, China
| | - Minjie Chen
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Ya Wen
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Hao Wu
- Department of Otolaryngology, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
| | - Li Wang
- Institutes of Biomedical Sciences and the Shanghai Key Laboratory of Medical Epigenetics, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yujuan Zhu
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Yile Ren
- Department of Rheumatology, Affiliated Municipal Hospital of Xuzhou Medical University, Xuzhou, 221100, China
| | - Zhifeng Gu
- Department of Rheumatology, Research Center of Immunology, Affiliated Hospital of Nantong University, Nantong University, Nantong, 226001, China
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Li S, Xu M, Meng Y, Sun H, Zhang T, Yang H, Li Y, Ma X. The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors. MEDCOMM – ONCOLOGY 2024; 3. [DOI: 10.1002/mog2.91] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 09/03/2024] [Indexed: 01/04/2025]
Abstract
AbstractGastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.
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Affiliation(s)
- Shen Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Maosen Xu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy, West China Hospital, National Clinical Research, Sichuan University Chengdu Sichuan China
| | - Yuanling Meng
- West China School of Stomatology Sichuan University Chengdu Sichuan China
| | - Haozhen Sun
- College of Life Sciences Sichuan University Chengdu Sichuan China
| | - Tao Zhang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Hanle Yang
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Yueyi Li
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
| | - Xuelei Ma
- Department of Biotherapy Cancer Center, West China Hospital, West China Medical School Sichuan University Chengdu China
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Lee H, Chung JW, Yun SC, Jung SW, Yoon YJ, Kim JH, Cha B, Kayasseh MA, Kim KO. Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy. Diagnostics (Basel) 2024; 14:2706. [PMID: 39682614 DOI: 10.3390/diagnostics14232706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Revised: 11/22/2024] [Accepted: 11/25/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND/OBJECTIVES Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON® in detecting gastric neoplasm. METHODS We used the retrospective data of 500 still images, including 5 benign gastric ulcers, 95 with gastric cancer, and 400 normal images. Thereby we validated the CADe algorithm measuring accuracy, sensitivity, and specificity with the result of receiver operating characteristic curves (ROC) and area under curve (AUC) in addition to comparing the diagnostic performance status of four expert endoscopists, four trainees, and four beginners from two university-affiliated hospitals with CADe algorithm. After a washing-out period of over 2 weeks, endoscopists performed gastric detection on the same dataset of the 500 endoscopic images again marked by ALPHAON®. RESULTS The CADe algorithm presented high validity in detecting gastric neoplasm with accuracy (0.88, 95% CI: 0.85 to 0.91), sensitivity (0.93, 95% CI: 0.88 to 0.98), specificity (0.87, 95% CI: 0.84 to 0.90), and AUC (0.962). After a washing-out period of over 2 weeks, overall validity improved in the trainee and beginner groups with the assistance of ALPHAON®. Significant improvement was present, especially in the beginner group (accuracy 0.94 (0.93 to 0.96) p < 0.001, sensitivity 0.87 (0.82 to 0.92) p < 0.001, specificity 0.96 (0.95 to 0.97) p < 0.001). CONCLUSIONS The high validation performance state of the CADe algorithm system was verified. Also, ALPHAON® has demonstrated its potential to serve as an endoscopic educator for beginners improving and making progress in sensitivity and specificity.
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Affiliation(s)
- Hannah Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea
| | - Sung-Cheol Yun
- Division of Biostatistics, Center for Medical Research and Information, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
| | - Sung Woo Jung
- Division of Gastroenterology, Department of Internal Medicine, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | | | - Ji Hee Kim
- CAIMI Co., Ltd., Incheon 22004, Republic of Korea
| | - Boram Cha
- Division of Gastroenterology, Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Incheon 22332, Republic of Korea
| | - Mohd Azzam Kayasseh
- Division of Gastroenterology, Dr. Sulaiman AI Habib Medical Group, Dubai Healthcare City, Dubai 51431, United Arab Emirates
| | - Kyoung Oh Kim
- Division of Gastroenterology, Department of Internal Medicine, Gachon University, Gil Medical Center, Incheon 21565, Republic of Korea
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Lu Y, Lu D, Li C, Chen L. Exploring Immune Cell Infiltration and Small Molecule Compounds for Ulcerative Colitis Treatment. Genes (Basel) 2024; 15:1548. [PMID: 39766817 PMCID: PMC11728156 DOI: 10.3390/genes15121548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Revised: 11/25/2024] [Accepted: 11/27/2024] [Indexed: 01/15/2025] Open
Abstract
BACKGROUND/OBJECTIVES Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) with a relapsing nature and complex etiology. Bioinformatics analysis has been widely applied to investigate various diseases. This study aimed to identify crucial differentially expressed genes (DEGs) and explore potential therapeutic agents for UC. METHODS The GSE47908 and GSE55306 colon tissue transcriptome gene datasets were downloaded from the Gene Expression Omnibus-NCBI (GEO) database. GEO2R and Gene Set Enrichment Analysis (GSEA) were used to screen for DEGs in patients with UC compared to the normal population based on weighted gene co-expression network analysis (WGCNA). GO-BP analysis and KEGG enrichment analysis were performed on the intersecting differential genes via the Metascape website, while hub genes were analyzed by STRING11.0 and Cytoscape3.7.1. The expression of hub genes was verified in the dataset GSE38713 colon tissue specimens. Finally, the gene expression profiles of the validation set were analyzed by immuno-infiltration through the ImmuCellAI online tool, and the CMap database was used to screen for negatively correlated small molecule compounds. RESULTS A total of 595 and 926 genes were screened by analysis of GSE47908 and GSE55306 datasets, respectively. Combined WGCNA hub module intersection yielded 12 hub genes (CXCL8, IL1β, CXCL1, CCL20, CXCL2, CXCR2, LCN2, SELL, AGT, LILRB3, MMP3, IDO1) associated with the pathogenesis of UC. GSEA analysis yielded intersecting pathways for both datasets (colorectal cancer pathway, base excision repair, cell cycle, apoptosis). GO-BP and KEGG enrichment analyses were performed to obtain key biological processes (inflammatory response, response to bacteria, leukocyte activation involved in the immune response, leukocyte-cell adhesion, apoptosis, positive regulation of immune effector processes) and key signaling pathways (cytokine-cytokine receptor interactions, IBD, NOD-like receptor signaling pathways). The immune cell infiltration analysis suggested that the incidence of UC was mainly related to the increase in CD4+T cells, depletion of T cells, T follicular helper cells, natural killer cells, γδ T cells and the decrease in CD8 naive T cells, helper T cells 17 and effector T cells. The CMap database results showed that small molecule compounds such as vorinostat, roxarsone, and wortmannin may be therapeutic candidates for UC. CONCLUSIONS This study not only aids in early prediction and prevention but also provides novel insights into the pathogenesis and treatment of UC.
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Affiliation(s)
- Yi Lu
- Shanghai Tufeng Pharmaceutical Technology Co., Ltd., Shanghai 201203, China
- Jiangsu Kanion Pharmaceutical Co., Ltd., Lianyungang 222001, China
| | - Dongqing Lu
- Department of Traditional Chinese Medicine, Beicai Community Health Service Center of Pudong New District, 271 Lianyuan Road, Pudong New District, Shanghai 201024, China
| | - Chujie Li
- Department of Pharmacology and Personalized Medicine, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- The M-Lab., Department of Precision Medicine, GROW—Research Institute for Oncology and Repro-Duction, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Luping Chen
- Department of Pharmacology and Toxicology, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6200 MD Maastricht, The Netherlands
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Pathipati MP, Shah ED. A Practical Approach to Navigating Relevance, Regulatory, and Reimbursement in Gastroenterology Innovation for Gastroenterology Trainees. Clin Gastroenterol Hepatol 2024; 22:2168-2171. [PMID: 39019081 PMCID: PMC11645659 DOI: 10.1016/j.cgh.2024.05.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 07/19/2024]
Affiliation(s)
- Mythili P Pathipati
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Center for Neurointestinal Health, Massachusetts General Hospital, Boston, Massachusetts
| | - Eric D Shah
- Division of Gastroenterology & Hepatology, Michigan Medicine, Ann Arbor, Michigan
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Kral J, Hradis M, Buzga M, Kunovsky L. Exploring the benefits and challenges of AI-driven large language models in gastroenterology: Think out of the box. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2024; 168:277-283. [PMID: 39234774 DOI: 10.5507/bp.2024.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 08/16/2024] [Indexed: 09/06/2024] Open
Abstract
Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.
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Affiliation(s)
- Jan Kral
- Department of Internal Medicine, University Hospital Motol and Second Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Hepatogastroenterology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Michal Hradis
- MAIA LABS s.r.o., Brno, Czech Republic
- Faculty of Information Technology, University of Technology, Brno, Czech Republic
| | - Marek Buzga
- Department of Physiology and Pathophysiology, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
- Institute of Laboratory Medicine, University Hospital Ostrava, Ostrava, Czech Republic
| | - Lumir Kunovsky
- 2nd Department of Internal Medicine - Gastroenterology and Geriatrics, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
- Department of Surgery, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Gastroenterology and Digestive Endoscopy, Masaryk Memorial Cancer Institute, Brno, Czech Republic
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Cheng Y, Li L, Bi Y, Su S, Zhang B, Feng X, Wang N, Zhang W, Yao Y, Ru N, Xiang J, Sun L, Hu K, Wen F, Wang Z, Bai L, Wang X, Wang R, Lv X, Wang P, Meng F, Xiao W, Linghu E, Chai N. Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging. Dig Liver Dis 2024; 56:1738-1745. [PMID: 38744557 DOI: 10.1016/j.dld.2024.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/21/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVES This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. METHODS We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. RESULTS The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. CONCLUSIONS The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.
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Affiliation(s)
- Yaxuan Cheng
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Longsong Li
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yawei Bi
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Song Su
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Bo Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xiuxue Feng
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nanjun Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Wengang Zhang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Yi Yao
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Nan Ru
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Jingyuan Xiang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lihua Sun
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Kang Hu
- Department of Gastroenterology, The 987 Hospital of PLA Joint Logistic Support Force, Baoji, 721004, PR China
| | - Feng Wen
- Department of Gastroenterology, General Hospital of Central Theater Command of PLA,Wuhan 430070, PR China
| | - Zixin Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Lu Bai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xueting Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Runzi Wang
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Xingping Lv
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Pengju Wang
- Chinese PLA Medical School, Beijing, 100853, PR China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China
| | - Fanqi Meng
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Wen Xiao
- Medical Department, HighWise Medical Technology Co, Ltd, Changsha, 410000, PR China
| | - Enqiang Linghu
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
| | - Ningli Chai
- Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, PR China.
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Peyrin‐Biroulet L, Adsul S, Stancati A, Dehmeshki J, Kubassova O. An artificial intelligence-driven scoring system to measure histological disease activity in ulcerative colitis. United European Gastroenterol J 2024; 12:1028-1033. [PMID: 38590110 PMCID: PMC11485311 DOI: 10.1002/ueg2.12562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/08/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND AND AIMS Assessment and scoring of histological images in Ulcerative colitis (UC) is prone to inter- and intra-observer variability. This study aimed to investigate whether an artificial intelligence (AI) system developed using image processing and machine learning algorithms could measure histological disease activity based on the Nancy index. METHODS A total of 200 histological images of patients with UC were used in this study. A novel AI algorithm was developed using state-of-the-art image processing and machine learning algorithms based on deep learning and feature extraction. The cell regions of each image, followed by the Nancy index, were manually annotated and measured independently by four histopathologists. Manual and AI-automated measurements of the Nancy index score were conducted and assessed using the intraclass correlation coefficient (ICC). RESULTS The 200-image dataset was divided into two groups (80% was used for training and 20% for testing). Intraclass correlation coefficient statistical analyses were performed to evaluate the AI tool and used as a reference to calculate the accuracy. The average ICC among the histopathologists was 89.3 and the average ICC between histopathologists and the AI tool was 87.2. The AI tool was found to be highly correlated with histopathologists. CONCLUSIONS The high correlation of performance of the AI method suggests promising potential for inflammatory bowel disease clinical applications. A standardized automated histological AI-driven scoring system can potentially be used in daily inflammatory bowel disease practice to reduce training needs and resource use, eliminate the subjectivity of the pathologists, and assess disease severity for treatment decisions.
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Affiliation(s)
- Laurent Peyrin‐Biroulet
- Department of GastroenterologyINFINY InstituteFHU‐CUREINSERM NGERENancy University HospitalVandoeuvre‐lès‐NancyFrance
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Chen D, Zhou R, Li B. Preoperative Prediction of Her-2 and Ki-67 Status in Gastric Cancer Using 18F-FDG PET/CT Radiomics Features of Visceral Adipose Tissue. Br J Hosp Med (Lond) 2024; 85:1-18. [PMID: 39347666 DOI: 10.12968/hmed.2024.0350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Aims/Background Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (Her-2) and Ki-67 expression levels. However, IHC is invasive and cannot reflect their expression status in real-time. This study aimed to build radiomics models based on visceral adipose tissue (VAT)'s 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging, and to evaluate the relationship between radiomics features of VAT and positive expression of Her-2 and Ki-67 in gastric cancer (GC). Methods Ninety patients with GC were enrolled in this study. 18F-FDG PET/CT radiomics features were calculated using the PyRadiomics package. Two methods were employed to reduce radiomics features. The machine learning models, logistic regression (LR), and support vector machine (SVM), were constructed and estimated by the receiver operator characteristic (ROC) curve. The correlation of outstanding features with Ki-67 and Her-2 expression status was evaluated. Results For the Ki-67 set, the area under of the receiver operator characteristic curve (AUC) and accuracy were 0.86 and 0.79 for the LR model and 0.83 and 0.69 for the SVM model. For the Her-2 set, the AUC and accuracy were 0.84 and 0.86 for the LR model and 0.65 and 0.85 for the SVM model. The LR model for Ki-67 exhibited outstanding prediction performance. Three wavelet transform features were correlated with Her-2 expression status (p all < 0.001), and one wavelet transform feature was correlated with the expression status of Ki-67 (p = 0.042). Conclusion 18F-FDG PET/CT-based radiomics models of VAT demonstrate good performance in predicting Her-2 and Ki-67 expression status in patients with GC. Radiomics features can be used as imaging biomarkers for GC.
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Affiliation(s)
- Demei Chen
- Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing, China
| | - Rui Zhou
- Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing, China
| | - Bo Li
- Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing, China
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Mascarenhas M, Martins M, Ribeiro T, Afonso J, Cardoso P, Mendes F, Cardoso H, Almeida R, Ferreira J, Fonseca J, Macedo G. Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications. Diagnostics (Basel) 2024; 14:2100. [PMID: 39335779 PMCID: PMC11431531 DOI: 10.3390/diagnostics14182100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 08/29/2024] [Accepted: 09/05/2024] [Indexed: 09/30/2024] Open
Abstract
The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
| | - Hélder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
| | - Rute Almeida
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - João Ferreira
- Department of Mechanic Engineering, Faculty of Engineering of University of Porto, 4200 427 Porto, Portugal
- DigestAID-Digestive Artificial Intelligence Development, 4200 427 Porto, Portugal
| | - João Fonseca
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine of University of Porto, 4200 427 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200 427 Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, 4200 427 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal
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Peng Z, Wang X, Li J, Sun J, Wang Y, Li Y, Li W, Zhang S, Wang X, Pei Z. Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023). Front Med (Lausanne) 2024; 11:1438979. [PMID: 39359927 PMCID: PMC11445022 DOI: 10.3389/fmed.2024.1438979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024] Open
Abstract
Introduction Artificial intelligence is already widely utilized in gastroenterology. This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications. Methods This investigation compiled all pertinent publications related to artificial intelligence in the context of gastrointestinal polyps and digestive endoscopy from 2003 to 2023 within the Web of Science Core Collection database. Furthermore, the study harnessed the tools CiteSpace, VOSviewer, GraphPad Prism and Scimago Graphica for visual data analysis. The study retrieved a total of 2,394 documents in the field of AI in digestive endoscopy and 628 documents specifically related to AI in digestive tract polyps. Results The United States and China are the primary contributors to research in both fields. Since 2019, studies on AI for digestive tract polyps have constituted approximately 25% of the total AI digestive endoscopy studies annually. Six of the top 10 most-cited studies in AI digestive endoscopy also rank among the top 10 most-cited studies in AI for gastrointestinal polyps. Additionally, the number of studies on AI-assisted polyp segmentation is growing the fastest, with significant increases in AI-assisted polyp diagnosis and real-time systems beginning after 2020. Discussion The application of AI in gastroenterology has garnered increasing attention. As theoretical advancements in AI for gastroenterology have progressed, real-time diagnosis and detection of gastrointestinal diseases have become feasible in recent years, highlighting the promising potential of AI in this field.
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Affiliation(s)
- Ziye Peng
- Medical School, Tianjin University, Tianjin, China
| | - Xiangyu Wang
- Medical School, Tianjin University, Tianjin, China
| | - Jiaxin Li
- Medical School, Tianjin University, Tianjin, China
| | - Jiayi Sun
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Yuwei Wang
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Yanru Li
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Wen Li
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Shuyi Zhang
- Department of Endoscopy, Tianjin Union Medical Center, Tianjin, China
| | - Ximo Wang
- Tianjin Third Central Hospital, Tianjin, China
| | - Zhengcun Pei
- Medical School, Tianjin University, Tianjin, China
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Lv P, Cao Z, Zhu Z, Xu X, Zhao Z. Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study. Open Med (Wars) 2024; 19:20241031. [PMID: 39291279 PMCID: PMC11406433 DOI: 10.1515/med-2024-1031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 08/07/2024] [Accepted: 08/12/2024] [Indexed: 09/19/2024] Open
Abstract
Background The efficacy of artificial neural network (ANN) models employing laboratory variables for predicting fatty liver disease (FLD) remains inadequately established. The study aimed to develop ANN models to precisely predict FLD. Methods Of 12,058 participants undergoing the initial FLD screening, 7,990 eligible participants were included. A total of 6,309 participants were divided randomly into the training (4,415 participants, 70%) and validation (1,894 participants, 30%) sets for developing prediction models. The performance of ANNs was additionally tested in the testing set (1,681 participants). The area under the receiver operating characteristic curve (AUROC) was employed to assess the models' performance. Results The 18-variable, 11-variable, 3-variable, and 2-variable models each achieved robust FLD prediction performance, with AUROCs over 0.92, 0.91, and 0.89 in the training, validation, and testing, respectively. Although slightly inferior to the other three models in performance (AUROC ranges: 0.89-0.92 vs 0.91-0.95), the 2-variable model showed 80.3% accuracy and 89.7% positive predictive value in the testing. Incorporating age and gender increased the AUROCs of the resulting 20-variable, 13-variable, 5-variable, and 4-variable models each to over 0.93, 0.92, and 0.91 in the training, validation, and testing, respectively. Conclusions Implementation of the ANN models could effectively predict FLD, with enhanced predictive performance via the inclusion of age and gender.
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Affiliation(s)
- Panpan Lv
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhen Cao
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhengqi Zhu
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Xiaoqin Xu
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
| | - Zhen Zhao
- Department of Clinical Laboratory, Minhang Hospital, Fudan University, Shanghai, China
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Liu H, Diao YK, Wei F, Wang SY, Liang YJ, Wu YF, Zheng QX, Wang XM, Wang H, Li J, Chen TH, Wu XC, Gu WM, Zhou YH, Guo HW, Shao GZ, Xu JH, Yao LQ, Wang MD, Shen F, Pawlik TM, Lau WY, Lv GY, Yang T. Stratifying risk of failure to achieve textbook outcomes among patients undergoing hepatectomy for hepatocellular carcinoma: A multicenter score validation study. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108477. [PMID: 38954879 DOI: 10.1016/j.ejso.2024.108477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Accepted: 06/08/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND AND AIMS The concept of textbook outcomes (TOs) has gained increased attention as a critical metric to assess the quality and success of outcomes following complex surgery. A simple yet effective scoring system was developed and validated to predict risk of not achieving textbook outcomes (non-TOs) following hepatectomy for hepatocellular carcinoma (HCC). METHODS Using a multicenter prospectively collected database, risk factors associated with non-TO among patients who underwent hepatectomy for HCC were identified. A predictive scoring system based on factors identified from multivariate regression analysis was used to risk stratify patients relative to non-TO. The score was developed using 70 % of the overall cohort and validated in the remaining 30 %. RESULTS Among 3681 patients, 1458 (39.6 %) failied to experience a TO. Based on the derivation cohort, obesity, American Society of Anaesthesiologists score(ASA score), Child-Pugh grade, tumor size, and extent of hepatectomy were identified as independent predictors of non-TO. The scoring system ranged from 0 to 10 points. Patients were categorized into low (0-3 points), intermediate (4-6 points), and high risk (7-10 points) of non-TO. In the validation cohort, the predicted risk of developing non-TOs was 39.0 %, which closely matched the observed risk of 39.9 %. There were no differences among the predicted and observed risks within the different risk categories. CONCLUSIONS A novel scoring system was able to predict risk of non-TO accurately following hepatectomy for HCC. The score may enable early identification of individuals at risk of adverse outcomes and inform surgical decision-making, and quality improvement initiatives.
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Affiliation(s)
- Han Liu
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Yong-Kang Diao
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Si-Yuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Ying-Jian Liang
- Department of Hepatobiliary Surgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yi-Fan Wu
- Department of Hepatobiliary Surgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China
| | - Qi-Xuan Zheng
- Department of Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Xian-Ming Wang
- Department of General Surgery, First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China
| | - Hong Wang
- Department of General Surgery, Liuyang People's Hospital, Liuyang, Hunan, China
| | - Jie Li
- Department of Hepatobiliary Surgery, Fuyang People's Hospital, Fuyang, Anhui, China
| | - Ting-Hao Chen
- Department of General Surgery, Ziyang First People's Hospital, Ziyang, Sichuan, China
| | - Xiao-Chang Wu
- Department of Hepatobiliary Surgery, Huzhou Central Hospital, Zhejiang University Huzhou Hospital, Huzhou, Zhejiang, China
| | - Wei-Min Gu
- The First Department of General Surgery, The Fourth Hospital of Harbin, Harbin, Heilongjiang, China
| | - Ya-Hao Zhou
- Department of Hepatobiliary Surgery, Pu'er People's Hospital, Pu'er, Yunnan, China
| | - Hong-Wei Guo
- The 2nd Department of General Surgery, The Second People's Hospital of Changzhi, Changzhi, China
| | - Guang-Zhao Shao
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Jia-Hao Xu
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Lan-Qing Yao
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Ming-Da Wang
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Feng Shen
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China
| | - Timothy M Pawlik
- Department of Surgery, Ohio State University, Wexner Medical Center, Columbus, OH, United States
| | - Wan Yee Lau
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China; Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Guo-Yue Lv
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Tian Yang
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China; Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Naval Medical University), Shanghai, China.
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Mansoor MA, Ibrahim AF, Kidd N. The Impact of Artificial Intelligence on Internal Medicine Physicians: A Survey of Procedural and Non-procedural Specialties. Cureus 2024; 16:e69121. [PMID: 39398704 PMCID: PMC11466679 DOI: 10.7759/cureus.69121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2024] [Indexed: 10/15/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly being integrated into various aspects of healthcare, including internal medicine. However, the impact of AI on physicians across different internal medicine specialties remains unclear. This study assesses AI's adoption, utilization, and perceived impact among procedural and non-procedural internal medicine physicians. METHODS A comprehensive survey questionnaire was designed to cover current AI use, perceived impact on diagnostic accuracy, treatment decisions, patient outcomes, challenges, ethical concerns, and future expectations. The survey was distributed to a diverse sample of internal medicine physicians across various specialties, including procedural (e.g., interventional cardiology, gastroenterology) and non-procedural (e.g., endocrinology, rheumatology) fields. Responses were analyzed using descriptive statistics, chi-square tests, t-tests, and logistic regression. RESULTS The survey received responses from 22 internal medicine physicians, with 64% (n=14) representing procedural specialties and 36% (n=8) representing non-procedural specialties. Sixty-eight percent (n=15) of respondents reported using AI tools in their practice, with higher adoption rates among procedural specialties (n=11, 79%) compared to non-procedural specialties (n=4, 50%). Surveyed physicians reported that AI improved diagnostic accuracy (n=12, 80%), treatment decisions (n=10, 67%), and patient outcomes (n=13, 87%). However, 55% (n=12) of respondents expressed concerns about the interpretability and transparency of AI algorithms. Non-procedural specialists were more likely to perceive AI as a threat to their job security (n=3, 38%) than procedural specialists (n=3, 21%). The most common challenges to AI adoption were lack of training (n=16, 73%), cost (n=13, 59%), and data privacy concerns (n=11, 50%). CONCLUSION This study assesses the perceived impact of AI on internal medicine physicians, highlighting the differences between procedural and non-procedural specialties. The findings underscore the need for specialty-specific considerations in developing and implementing AI tools. While AI can potentially improve diagnostic accuracy, treatment decisions, and patient outcomes, addressing challenges such as lack of training, cost, and data privacy concerns is crucial for widespread adoption. Moreover, the study emphasizes the importance of ensuring the interpretability and transparency of AI algorithms to foster trust among physicians. As AI continues to evolve, it is essential to engage internal medicine physicians across specialties in the development process to create AI tools that effectively complement their expertise and improve patient care. Further research should focus on developing best practices for AI integration in internal medicine and evaluating the long-term impact on patient outcomes and healthcare systems.
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Affiliation(s)
- Masab A Mansoor
- Internal Medicine, Edward Via College of Osteopathic Medicine, Monroe, USA
| | - Andrew F Ibrahim
- School of Medicine, Texas Tech University Health Sciences Center, Lubbock, USA
| | - Nicholas Kidd
- Family Medicine, University of Virginia, Charlottesville, USA
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Mikkola M, Desmet KLJ, Kommisrud E, Riegler MA. Recent advancements to increase success in assisted reproductive technologies in cattle. Anim Reprod 2024; 21:e20240031. [PMID: 39176005 PMCID: PMC11340803 DOI: 10.1590/1984-3143-ar2024-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/14/2024] [Indexed: 08/24/2024] Open
Abstract
Assisted reproductive technologies (ART) are fundamental for cattle breeding and sustainable food production. Together with genomic selection, these technologies contribute to reducing the generation interval and accelerating genetic progress. In this paper, we discuss advancements in technologies used in the fertility evaluation of breeding animals, and the collection, processing, and preservation of the gametes. It is of utmost importance for the breeding industry to select dams and sires of the next generation as young as possible, as is the efficient and timely collection of gametes. There is a need for reliable and easily applicable methods to evaluate sexual maturity and fertility. Although gametes processing and preservation have been improved in recent decades, challenges are still encountered. The targeted use of sexed semen and beef semen has obliterated the production of surplus replacement heifers and bull calves from dairy breeds, markedly improving animal welfare and ethical considerations in production practices. Parallel with new technologies, many well-established technologies remain relevant, although with evolving applications. In vitro production (IVP) has become the predominant method of embryo production. Although fundamental improvements in IVP procedures have been established, the quality of IVP embryos remains inferior to their in vivo counterparts. Improvements to facilitate oocyte maturation and development of new culture systems, e.g. microfluidics, are presented in this paper. New non-invasive and objective tools are needed to select embryos for transfer. Cryopreservation of semen and embryos plays a pivotal role in the distribution of genetics, and we discuss the challenges and opportunities in this field. Finally, machine learning (ML) is gaining ground in agriculture and ART. This paper delves into the utilization of emerging technologies in ART, along with the current status, key challenges, and future prospects of ML in both research and practical applications within ART.
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Affiliation(s)
| | | | - Elisabeth Kommisrud
- CRESCO, Centre for Embryology and Healthy Development, Department of Biotechnology, Inland Norway University of Applied Sciences, Hamar, Norway
| | - Michael A. Riegler
- Holistic Systems Department, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
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Huang W, Peng Y, Kang L. Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future. VIEW 2024; 5. [DOI: 10.1002/viw.20240010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 06/28/2024] [Indexed: 01/04/2025] Open
Abstract
AbstractLiver fibrosis is a reparative response triggered by liver injury. Non‐invasive assessment and staging of liver fibrosis in patients with chronic liver disease are of paramount importance, as treatment strategies and prognoses depend significantly on the degree of fibrosis. Although liver fibrosis has traditionally been staged through invasive liver biopsy, this method is prone to sampling errors, particularly when biopsy sizes are inadequate. Consequently, there is an urgent clinical need for an alternative to biopsy, one that ensures precise, sensitive, and non‐invasive diagnosis and staging of liver fibrosis. Non‐invasive imaging assessments have assumed a pivotal role in clinical practice, enjoying growing popularity and acceptance due to their potential for diagnosing, staging, and monitoring liver fibrosis. In this comprehensive review, we first delved into the current landscape of non‐invasive imaging technologies, assessing their accuracy and the transformative impact they have had on the diagnosis and management of liver fibrosis in both clinical practice and animal models. Additionally, we provided an in‐depth exploration of recent advancements in ultrasound imaging, computed tomography imaging, magnetic resonance imaging, nuclear medicine imaging, radiomics, and artificial intelligence within the field of liver fibrosis research. We summarized the key concepts, advantages, limitations, and diagnostic performance of each technique. Finally, we discussed the challenges associated with clinical implementation and offer our perspective on advancing the field, hoping to provide alternative directions for the future research.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine Peking University First Hospital Beijing China
| | - Yushuo Peng
- Department of Nuclear Medicine Peking University First Hospital Beijing China
| | - Lei Kang
- Department of Nuclear Medicine Peking University First Hospital Beijing China
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Piao Z, Meng M, Yang H, Xue T, Jia Z, Liu W. Distinguishing between aldosterone-producing adenomas and non-functional adrenocortical adenomas using the YOLOv5 network. Acta Radiol 2024; 65:1007-1014. [PMID: 38767055 DOI: 10.1177/02841851241251446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
BACKGROUND You Only Look Once version 5 (YOLOv5), a one-stage deep-learning (DL) algorithm for object detection and classification, offers high speed and accuracy for identifying targets. PURPOSE To investigate the feasibility of using the YOLOv5 algorithm to non-invasively distinguish between aldosterone-producing adenomas (APAs) and non-functional adrenocortical adenomas (NF-ACAs) on computed tomography (CT) images. MATERIAL AND METHODS A total of 235 patients who were diagnosed with ACAs between January 2011 and July 2022 were included in this study. Of the 215 patients, 81 (37.7%) had APAs and 134 (62.3%) had NF-ACAs' they were randomly divided into either the training set or the validation set at a ratio of 9:1. Another 20 patients, including 8 (40.0%) with APA and 12 (60.0%) with NF-ACA, were collected for the testing set. Five submodels (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) of YOLOv5 were trained and evaluated on the datasets. RESULTS In the testing set, the mAP_0.5 value for YOLOv5x (0.988) was higher than the values for YOLOv5n (0.969), YOLOv5s (0.965), YOLOv5m (0.974), and YOLOv5l (0.983). The mAP_0.5:0.95 value for YOLOv5x (0.711) was also higher than the values for YOLOv5n (0.587), YOLOv5s (0.674), YOLOv5m (0.671), and YOLOv5l (0.698) in the testing set. The inference speed of YOLOv5n was 2.4 ms in the testing set, which was the fastest among the five submodels. CONCLUSION The YOLOv5 algorithm can accurately and efficiently distinguish between APAs and NF-ACAs on CT images, especially YOLOv5x has the best identification performance.
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Affiliation(s)
- Zeyu Piao
- Graduate College, Dalian Medical University, Dalian, PR China
- Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Mingzhu Meng
- Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Huijie Yang
- Department of Endocrinology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Tongqing Xue
- Department of Interventional Radiology, Huaian Hospital of Huai'an City, Huai'an, PR China
| | - Zhongzhi Jia
- Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
| | - Wei Liu
- Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, PR China
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Gupta P, Basu S, Arora C. Applications of artificial intelligence in biliary tract cancers. Indian J Gastroenterol 2024; 43:717-728. [PMID: 38427281 DOI: 10.1007/s12664-024-01518-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/29/2023] [Indexed: 03/02/2024]
Abstract
Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
| | - Soumen Basu
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology - Delhi, New Delhi, 110 016, India
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Nam SJ, Moon G, Park JH, Kim Y, Lim YJ, Choi HS. Deep Learning-Based Real-Time Organ Localization and Transit Time Estimation in Wireless Capsule Endoscopy. Biomedicines 2024; 12:1704. [PMID: 39200169 PMCID: PMC11351118 DOI: 10.3390/biomedicines12081704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/16/2024] [Accepted: 07/22/2024] [Indexed: 09/02/2024] Open
Abstract
BACKGROUND Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. METHODS We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients. RESULTS Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. CONCLUSIONS The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.
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Affiliation(s)
- Seung-Joo Nam
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24341, Republic of Korea
| | - Gwiseong Moon
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
| | | | - Yoon Kim
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
- Department of Computer Science and Engineering, College of IT, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Yun Jeong Lim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang 10326, Republic of Korea
| | - Hyun-Soo Choi
- Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
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Uher O, Hadrava Vanova K, Taïeb D, Calsina B, Robledo M, Clifton-Bligh R, Pacak K. The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives. Endocr Rev 2024; 45:521-552. [PMID: 38377172 PMCID: PMC11244254 DOI: 10.1210/endrev/bnae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/19/2023] [Accepted: 02/02/2024] [Indexed: 02/22/2024]
Abstract
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.
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Affiliation(s)
- Ondrej Uher
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
| | - Katerina Hadrava Vanova
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
| | - David Taïeb
- Department of Nuclear Medicine, CHU de La Timone, Marseille 13005, France
| | - Bruna Calsina
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
- Familiar Cancer Clinical Unit, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Institute of Health Carlos III (ISCIII), Madrid 28029, Spain
| | - Roderick Clifton-Bligh
- Department of Endocrinology, Royal North Shore Hospital, Sydney 2065, NSW, Australia
- Cancer Genetics Laboratory, Kolling Institute, University of Sydney, Sydney 2065, NSW, Australia
| | - Karel Pacak
- Section of Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-1109, USA
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Chen S, Zhuang D, Jia Q, Guo B, Hu G. Advances in Noninvasive Molecular Imaging Probes for Liver Fibrosis Diagnosis. Biomater Res 2024; 28:0042. [PMID: 38952717 PMCID: PMC11214848 DOI: 10.34133/bmr.0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/08/2024] [Indexed: 07/03/2024] Open
Abstract
Liver fibrosis is a wound-healing response to chronic liver injury, which may lead to cirrhosis and cancer. Early-stage fibrosis is reversible, and it is difficult to precisely diagnose with conventional imaging modalities such as magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, and ultrasound imaging. In contrast, probe-assisted molecular imaging offers a promising noninvasive approach to visualize early fibrosis changes in vivo, thus facilitating early diagnosis and staging liver fibrosis, and even monitoring of the treatment response. Here, the most recent progress in molecular imaging technologies for liver fibrosis is updated. We start by illustrating pathogenesis for liver fibrosis, which includes capillarization of liver sinusoidal endothelial cells, cellular and molecular processes involved in inflammation and fibrogenesis, as well as processes of collagen synthesis, oxidation, and cross-linking. Furthermore, the biological targets used in molecular imaging of liver fibrosis are summarized, which are composed of receptors on hepatic stellate cells, macrophages, and even liver collagen. Notably, the focus is on insights into the advances in imaging modalities developed for liver fibrosis diagnosis and the update in the corresponding contrast agents. In addition, challenges and opportunities for future research and clinical translation of the molecular imaging modalities and the contrast agents are pointed out. We hope that this review would serve as a guide for scientists and students who are interested in liver fibrosis imaging and treatment, and as well expedite the translation of molecular imaging technologies from bench to bedside.
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Affiliation(s)
- Shaofang Chen
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Danping Zhuang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Qingyun Jia
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application,
Harbin Institute of Technology, Shenzhen 518055, China
| | - Genwen Hu
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
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Guo Z, Zhang Z, Liu L, Zhao Y, Liu Z, Zhang C, Qi H, Feng J, Yang C, Tai W, Banchini F, Inchingolo R. Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108362. [PMID: 38704899 DOI: 10.1016/j.ejso.2024.108362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 04/11/2024] [Accepted: 04/20/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC). METHODS Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients. RESULTS 51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611. CONCLUSION In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.
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Affiliation(s)
- Zhentian Guo
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zongming Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China.
| | - Limin Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Yue Zhao
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zhuo Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Chong Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Hui Qi
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Jinqiu Feng
- Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China; Department of Immunology, Peking University School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Chunmin Yang
- Department of Gastroenterology, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China
| | - Weiping Tai
- Department of Gastroenterology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Filippo Banchini
- General Surgery Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, "F. Miulli" Regional General Hospital, Acquaviva delle Fonti, 70021, Italy
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Park MA, Whelan CJ, Ahmed S, Boeringer T, Brown J, Carson TL, Crowder SL, Gage K, Gregg C, Jeong DK, Jim HSL, Judge AR, Mason TM, Parker N, Pillai S, Qayyum A, Rajasekhara S, Rasool G, Tinsley SM, Schabath MB, Stewart P, West J, McDonald P, Permuth JB. Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration. Cancers (Basel) 2024; 16:2364. [PMID: 39001427 PMCID: PMC11240731 DOI: 10.3390/cancers16132364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.
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Affiliation(s)
- Margaret A. Park
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Christopher J. Whelan
- Department of Metabolism and Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Sabeen Ahmed
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.A.); (G.R.)
| | - Tabitha Boeringer
- Department of Drug Discovery, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.B.); (S.P.)
| | - Joel Brown
- Department of Cancer Biology and Evolution, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (J.B.); (J.W.)
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Tiffany L. Carson
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.L.C.); (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Sylvia L. Crowder
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.L.C.); (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Kenneth Gage
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (K.G.); (D.K.J.); (A.Q.)
| | - Christopher Gregg
- School of Medicine, University of Utah, Salt Lake City, UT 84113, USA;
| | - Daniel K. Jeong
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (K.G.); (D.K.J.); (A.Q.)
| | - Heather S. L. Jim
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.L.C.); (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Andrew R. Judge
- Department of Physical Therapy, University of Florida, Gainesville, FL 32610, USA;
| | - Tina M. Mason
- Department of Nursing Research, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Nathan Parker
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.L.C.); (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
| | - Smitha Pillai
- Department of Drug Discovery, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.B.); (S.P.)
| | - Aliya Qayyum
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (K.G.); (D.K.J.); (A.Q.)
| | - Sahana Rajasekhara
- Department of Supportive Care Medicine, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Ghulam Rasool
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (S.A.); (G.R.)
| | - Sara M. Tinsley
- Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (T.L.C.); (S.L.C.); (H.S.L.J.); (N.P.); (S.M.T.)
- Department of Malignant Hematology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Paul Stewart
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Jeffrey West
- Department of Cancer Biology and Evolution, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (J.B.); (J.W.)
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Patricia McDonald
- Department of Metabolism and Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Lexicon Pharmaceuticals, Inc., Woodlands, TX 77381, USA
| | - Jennifer B. Permuth
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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Mamone G, Comelli A, Porrello G, Milazzo M, Di Piazza A, Stefano A, Benfante V, Tuttolomondo A, Sparacia G, Maruzzelli L, Miraglia R. Radiomics Analysis of Preprocedural CT Imaging for Outcome Prediction after Transjugular Intrahepatic Portosystemic Shunt Creation. Life (Basel) 2024; 14:726. [PMID: 38929709 PMCID: PMC11204649 DOI: 10.3390/life14060726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using "controlled expansion covered stents". MATERIALS AND METHODS This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients' outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. RESULTS A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the "clinical response" and "survival at 6 months" outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. CONCLUSIONS A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients.
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Affiliation(s)
- Giuseppe Mamone
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (V.B.)
| | - Giorgia Porrello
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D), University of Palermo, Via del Vespro 127, 90127 Palermo, Italy;
| | - Mariapina Milazzo
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Ambra Di Piazza
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
| | - Viviana Benfante
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy; (A.C.); (V.B.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy;
| | - Antonino Tuttolomondo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127 Palermo, Italy;
| | - Gianvincenzo Sparacia
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Luigi Maruzzelli
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
| | - Roberto Miraglia
- Radiology Unit, IRCCS-ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy; (M.M.); (A.D.P.); (G.S.); (L.M.); (R.M.)
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Zheng R, Su R, Fan Y, Xing F, Huang K, Yan F, Chen H, Liu B, Fang L, Du Y, Zhou F, Wang D, Feng S. Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures. Anal Chem 2024; 96:8772-8781. [PMID: 38743842 DOI: 10.1021/acs.analchem.4c01171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.
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Affiliation(s)
- Ran Zheng
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Rui Su
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Yusi Fan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130021, China
| | - Fan Xing
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Keke Huang
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Fei Yan
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Huanwen Chen
- School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang 330004, China
| | - Botong Liu
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Laiping Fang
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
| | - Yechao Du
- Department of General Surgery Center, First Hospital of Jilin University, 1 Xinmin Street Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130021, China
| | - Daguang Wang
- Department of Gastric Colorectal and Anal Surgery, First Hospital of Jilin University, 1 Xinmin Street Changchun, Jilin 130012, China
| | - Shouhua Feng
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China
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Coban S, Zahid KS, Brugge WR. The future of EUS. ENDOSCOPIC ULTRASONOGRAPHY 2024:287-293. [DOI: 10.1002/9781119697893.ch31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]
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Cardoso P, Mascarenhas M, Afonso J, Ribeiro T, Mendes F, Martins M, Andrade P, Cardoso H, Mascarenhas Saraiva M, Ferreira JP, Macedo G. Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network. Therap Adv Gastroenterol 2024; 17:17562848241251569. [PMID: 38812708 PMCID: PMC11135072 DOI: 10.1177/17562848241251569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 04/14/2024] [Indexed: 05/31/2024] Open
Abstract
Background Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.
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Affiliation(s)
- Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João P.S. Ferreira
- Faculty of Engineering, University of Porto, Porto, Portugal
- Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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Uchikov P, Khalid U, Vankov N, Kraeva M, Kraev K, Hristov B, Sandeva M, Dragusheva S, Chakarov D, Petrov P, Dobreva-Yatseva B, Novakov I. The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis. Diagnostics (Basel) 2024; 14:1004. [PMID: 38786302 PMCID: PMC11119852 DOI: 10.3390/diagnostics14101004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. METHOD A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. RESULTS Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. CONCLUSIONS When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.
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Affiliation(s)
- Petar Uchikov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (P.U.); (I.N.)
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Nikola Vankov
- University Multiprofile Hospital for Active Treatment “Saint George”, 4000 Plovdiv, Bulgaria;
| | - Maria Kraeva
- Department of Otorhynolaryngology, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Krasimir Kraev
- Department of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Bozhidar Hristov
- Section “Gastroenterology”, Second Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Milena Sandeva
- Department of Midwifery, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Snezhanka Dragusheva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
- Department of Anesthesiology, Emergency and Intensive Care Medicine, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Dzhevdet Chakarov
- Department of Propaedeutics of Surgical Diseases, Section of General Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Petko Petrov
- Department of Maxillofacial Surgery, Faculty of Dental Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Bistra Dobreva-Yatseva
- Section “Cardiology”, First Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Ivan Novakov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (P.U.); (I.N.)
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Gupta P, Basu S, Rana P, Dutta U, Soundararajan R, Kalage D, Chhabra M, Singh S, Yadav TD, Gupta V, Kaman L, Das CK, Gupta P, Saikia UN, Srinivasan R, Sandhu MS, Arora C. Deep-learning enabled ultrasound based detection of gallbladder cancer in northern India: a prospective diagnostic study. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2024; 24:100279. [PMID: 38756152 PMCID: PMC11096661 DOI: 10.1016/j.lansea.2023.100279] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/16/2023] [Accepted: 08/30/2023] [Indexed: 05/18/2024]
Abstract
Background Gallbladder cancer (GBC) is highly aggressive. Diagnosis of GBC is challenging as benign gallbladder lesions can have similar imaging features. We aim to develop and validate a deep learning (DL) model for the automatic detection of GBC at abdominal ultrasound (US) and compare its diagnostic performance with that of radiologists. Methods In this prospective study, a multiscale, second-order pooling-based DL classifier model was trained (training and validation cohorts) using the US data of patients with gallbladder lesions acquired between August 2019 and June 2021 at the Postgraduate Institute of Medical Education and research, a tertiary care hospital in North India. The performance of the DL model to detect GBC was evaluated in a temporally independent test cohort (July 2021-September 2022) and was compared with that of two radiologists. Findings The study included 233 patients in the training set (mean age, 48 ± (2SD) 23 years; 142 women), 59 patients in the validation set (mean age, 51.4 ± 19.2 years; 38 women), and 273 patients in the test set (mean age, 50.4 ± 22.1 years; 177 women). In the test set, the DL model had sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 92.3% (95% CI, 88.1-95.6), 74.4% (95% CI, 65.3-79.9), and 0.887 (95% CI, 0.844-0.930), respectively for detecting GBC which was comparable to both the radiologists. The DL-based approach showed high sensitivity (89.8-93%) and AUC (0.810-0.890) for detecting GBC in the presence of stones, contracted gallbladders, lesion size <10 mm, and neck lesions, which was comparable to both the radiologists (p = 0.052-0.738 for sensitivity and p = 0.061-0.745 for AUC). The sensitivity for DL-based detection of mural thickening type of GBC was significantly greater than one of the radiologists (87.8% vs. 72.8%, p = 0.012), despite a reduced specificity. Interpretation The DL-based approach demonstrated diagnostic performance comparable to experienced radiologists in detecting GBC using US. However, multicentre studies are warranted to explore the potential of DL-based diagnosis of GBC fully. Funding None.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Soumen Basu
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, 110016, India
| | - Pratyaksha Rana
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Usha Dutta
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Raghuraman Soundararajan
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Daneshwari Kalage
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Manika Chhabra
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Shravya Singh
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Thakur Deen Yadav
- Department of Surgical Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Vikas Gupta
- Department of Surgical Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Lileswar Kaman
- Department of General Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Chandan Krushna Das
- Department of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Parikshaa Gupta
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Uma Nahar Saikia
- Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Radhika Srinivasan
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Manavjit Singh Sandhu
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Chetan Arora
- Department of Computer Science and Engineering, Indian Institute of Technology, New Delhi, 110016, India
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Takabayashi K, Kobayashi T, Matsuoka K, Levesque BG, Kawamura T, Tanaka K, Kadota T, Bise R, Uchida S, Kanai T, Ogata H. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. Dig Endosc 2024; 36:582-590. [PMID: 37690125 DOI: 10.1111/den.14677] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVES Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. However, inflammatory bowel disease (IBD) expert endoscopists categorize the severity and diagnose the overall impression of the degree of inflammation. This study aimed to develop an artificial intelligence (AI) system that can accurately represent the assessment of the endoscopic severity of UC by IBD expert endoscopists. METHODS A ranking-convolutional neural network (ranking-CNN) was trained using comparative information on the UC severity of 13,826 pairs of endoscopic images created by IBD expert endoscopists. Using the trained ranking-CNN, the UC Endoscopic Gradation Scale (UCEGS) was used to express severity. Correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using UCEGS diagnosed by the AI and the Mayo Endoscopic Subscore, and the correlation coefficients of the mean for test images assessed using UCEGS by four IBD expert endoscopists and the AI. RESULTS Spearman's correlation coefficient between the UCEGS diagnosed by AI and Mayo Endoscopic Subscore was approximately 0.89. The correlation coefficients between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (P < 0.01). CONCLUSIONS The AI developed here can diagnose UC severity endoscopically similar to IBD expert endoscopists.
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Affiliation(s)
- Kaoru Takabayashi
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
| | - Taku Kobayashi
- Center for Advanced IBD Research and Treatment, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Katsuyoshi Matsuoka
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Sakura Medical Center, Chiba, Japan
| | - Barrett G Levesque
- Division of Gastroenterology, Los Angeles County/University of Southern California Medical Center, Los Angeles, USA
| | - Takuji Kawamura
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, Kyoto, Japan
| | - Kiyohito Tanaka
- Department of Gastroenterology, Kyoto Second Red Cross Hospital, Kyoto, Japan
| | - Takeaki Kadota
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Ryoma Bise
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Seiichi Uchida
- Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Haruhiko Ogata
- Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan
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