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Rath MM, Anirvan P, Varghese J, Tripathy TP, Patel RK, Panigrahi MK, Giri S. Comparison of standard vs auxiliary (contrast or elastography) endoscopic ultrasound-guided fine needle aspiration/biopsy in solid pancreatic lesions: A meta-analysis. World J Methodol 2025; 15:97415. [DOI: 10.5662/wjm.v15.i3.97415] [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: 05/29/2024] [Revised: 11/03/2024] [Accepted: 12/02/2024] [Indexed: 03/06/2025] Open
Abstract
BACKGROUND Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) is the most common modality for tissue acquisition from pancreatic masses. Despite high specificity, sensitivity remains less than 90%. Auxiliary techniques like elastography and contrast-enhanced EUS may guide tissue acquisition from viable tumor tissue and improve the diagnostic outcomes theoretically. However, data regarding the same have shown conflicting results.
AIM To compare the diagnostic outcomes of auxiliary-EUS-FNA/B to standard EUS-FNA/B for pancreatic lesions.
METHODS The electronic databases of MEDLINE, EMBASE, and Scopus were searched from inception to February 2024 for all relevant studies comparing diagnostic outcomes of auxiliary-EUS-FNA/B to standard EUS-FNA/B for pancreatic lesions. A bivariate hierarchical model was used to perform the meta-analysis.
RESULTS A total of 10 studies were identified. The pooled sensitivity, specificity, and area under the receiver-operated curve (AUROC) for standard EUS-FNA/B were 0.82 (95%CI: 0.79-0.85), 1.00 (95%CI: 0.96-1.00), and 0.97 (95%CI: 0.95-0.98), respectively. The pooled sensitivity, specificity, and AUROC for EUS-FNA/B with auxiliary techniques were 0.86 (95%CI: 0.83-0.89), 1.00 (95%CI: 0.94-1.00), and 0.96 (95%CI: 0.94-0.98), respectively. Comparing the two diagnostic modalities, sensitivity [Risk ratio (RR): 1.04, 95%CI: 0.99-1.09], specificity (RR: 1.00, 95%CI: 0.99-1.01), and diagnostic accuracy (RR: 1.03, 95%CI: 0.98-1.09) were comparable.
CONCLUSION Analysis of the currently available literature did not show any additional advantage of EUS-FNA/B with auxiliary techniques for pancreatic solid lesions over standard EUS-FNA/B. Further randomized studies are required to demonstrate the benefit of auxiliary techniques before they can be recommended for routine practice.
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Affiliation(s)
- Mitali Madhumita Rath
- Department of Pathology, IMS and SUM Hospital Campus-2, Bhubaneshwar 754001, Odisha, India
| | - Prajna Anirvan
- Department of Gastroenterology, Kalinga Gastroenterology Foundation, Cuttack 753001, Odisha, India
| | - Jijo Varghese
- Department of Gastroenterology, NS Memorial Institute of Medical Sciences, Kollam 691020, Kerala, India
| | - Tara Prasad Tripathy
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneshwar 751019, Odisha, India
| | - Ranjan K Patel
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneshwar 751019, Odisha, India
| | - Manas Kumar Panigrahi
- Department of Gastroenterology, All India Institute of Medical Sciences, Bhubaneshwar 751019, Odisha, India
| | - Suprabhat Giri
- Department of Gastroenterology and Hepatology, Kalinga Institute of Medical Sciences, Bhubaneswar 751024, Odisha, India
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Li X, Yao L, Wu H, Tan W, Zhou W, Zhang J, Dong Z, Ding X, Yu H. A deep learning-based, real-time image report system for linear EUS. Gastrointest Endosc 2025; 101:1166-1173.e11. [PMID: 39427992 DOI: 10.1016/j.gie.2024.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 08/01/2024] [Accepted: 10/12/2024] [Indexed: 10/22/2024]
Abstract
BACKGROUND AND AIMS The integrity of image acquisition is critical for biliopancreatic EUS reporting, significantly affecting the quality of EUS examinations and disease-related decision-making. However, the quality of EUS reports varies among endoscopists. To address this issue, we developed a deep learning-based EUS automatic image report system (EUS-AIRS), aiming to achieve automatic photodocumentation in real-time during EUS, including capturing standard stations, lesions, and puncture procedures. METHODS Eight deep learning models trained and tested using 235,784 images were integrated to construct the EUS-AIRS. The performance of EUS-AIRS was tested through man-machine comparisons at 2 levels: a retrospective test (include internal and external testing) and a prospective test. From May 2023 to October 2023, a total of 114 patients undergoing EUS at Renmin Hospital of Wuhan University were consecutively recruited for prospective testing. The primary outcome was the completeness of the EUS-AIRS for capturing standard stations. RESULTS In terms of completeness in capturing biliopancreatic standard stations, EUS-AIRS exceeded the capabilities of endoscopists at all levels of expertise in retrospective internal testing (90.8% [95% confidence interval (CI), 88.7%-92.9%] vs 70.5% [95% CI, 67.2%-73.8%]; P < .001) and external testing (91.4% [95% CI, 88.4%-94.4%] vs 68.2% [95% CI, 63.3%-73.2%]; P < .001). EUS-AIRS exhibited high accuracy and completeness in capturing standard station images. The completeness of the EUS-AIRS significantly outperformed manual endoscopist reports (91.4% [95% CI, 89.4%-93.4%] vs 78.1% [95% CI, 75.1%-81.0%); P < .001). CONCLUSIONS EUS-AIRS exhibits exceptional capabilities in real-time, capturing high-quality and high-integrity biliopancreatic EUS images. This showcases the potential of applying an artificial intelligence image report system in the EUS field.
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Affiliation(s)
- Xun Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Huiling Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Tan
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiangwu Ding
- Digestive Endoscopy Center, Wuhan Fourth Hospital, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
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Robles-Medranda C, Verpalen I, Schulz D, Spadaccini M. Artificial Intelligence in Biliopancreatic Disorders: Applications in Cross-Imaging and Endoscopy. Gastroenterology 2025:S0016-5085(25)00648-1. [PMID: 40311821 DOI: 10.1053/j.gastro.2025.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 03/15/2025] [Accepted: 04/11/2025] [Indexed: 05/03/2025]
Abstract
This review explores the transformative potential of artificial intelligence (AI) in the diagnosis and management of biliopancreatic disorders. By leveraging cutting-edge techniques, such as deep learning and convolutional neural networks, AI has significantly advanced gastroenterology, particularly in endoscopic procedures, such as colonoscopy; upper endoscopy; and capsule endoscopy. These applications enhance adenoma detection rates and improve lesion characterization and diagnostic accuracy. AI's integration in cross-sectional imaging modalities, such as computed tomography and magnetic resonance imaging, has remarkable potential. Models have demonstrated high accuracy in identifying pancreatic ductal adenocarcinoma; pancreatic cystic lesions; and pancreatic neuroendocrine tumors, aiding in early diagnosis; resectability assessment; and personalized treatment planning. In advanced endoscopic procedures, such as digital single-operator cholangioscopy and endoscopic ultrasound, AI enhances anatomic recognition and improves lesion classification, with a potential for reduction in procedural variability, enabling more consistent diagnostic and therapeutic outcomes. Promising applications in biliopancreatic endoscopy include the detection of biliary stenosis, classification of dysplastic precursor lesions, and assessment of pancreatic abnormalities. This review aims to capture the current state of AI application in biliopancreatic disorders, summarizing the results of early studies and paving the path for future directions.
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Affiliation(s)
| | - Inez Verpalen
- Amsterdam University Medical Center, Amsterdam, The Netherlands
| | | | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy; Humanitas Clinical and Research Center, Endoscopy Unit, Scientific Institute for Research, Hospitalization and Healthcare, Rozzano, Italy
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Carrara S, Andreozzi M, Terrin M, Spadaccini M. Role of Artificial Intelligence for Endoscopic Ultrasound. Gastrointest Endosc Clin N Am 2025; 35:407-418. [PMID: 40021237 DOI: 10.1016/j.giec.2024.10.007] [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
Endoscopic ultrasound (EUS) is widely used for the diagnosis of biliopancreatic and gastrointestinal tract diseases, but it is one of the most operator-dependent endoscopic techniques, requiring a long and complex learning curve. The role of artificial intelligence (AI) in EUS is growing as AI algorithms can assist in lesion detection and characterization by analyzing EUS images. Deep learning (DL) techniques, such as convolutional neural networks, have shown great potential for tumor identification; the application of AI models can increase the EUS diagnostic accuracy, provide faster diagnoses, and provide more information that can be helpful also for a training program.
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Affiliation(s)
- Silvia Carrara
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy.
| | - Marta Andreozzi
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Maria Terrin
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
| | - Marco Spadaccini
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
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K.V C, King DRGG. Automated detection of pancreatic cancer with segmentation and classification using fusion of UNET and CNN through spider monkey optimization. Biomed Signal Process Control 2025; 102:107413. [DOI: 10.1016/j.bspc.2024.107413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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Araújo CC, Frias J, Mendes F, Martins M, Mota J, Almeida MJ, Ribeiro T, Macedo G, Mascarenhas M. Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease. Cancers (Basel) 2025; 17:1132. [PMID: 40227709 PMCID: PMC11988021 DOI: 10.3390/cancers17071132] [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: 02/14/2025] [Accepted: 03/03/2025] [Indexed: 04/15/2025] Open
Abstract
Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI's applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns-like data privacy and algorithmic bias-must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.
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Affiliation(s)
- Catarina Cardoso Araújo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Joana Frias
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 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; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 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; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 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; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 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; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (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 Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (C.C.A.); (J.F.); (F.M.); (M.M.); (J.M.); (M.J.A.); (T.R.); (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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Mo S, Yi N, Qin F, Zhao H, Wang Y, Qin H, Wei H, Jiang H, Qin S. EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer. Front Oncol 2025; 15:1442209. [PMID: 40104505 PMCID: PMC11913666 DOI: 10.3389/fonc.2025.1442209] [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: 07/04/2024] [Accepted: 02/03/2025] [Indexed: 03/20/2025] Open
Abstract
Objectives This study aimed to develop and validate intratumoral, peritumoral, and combined radiomic models based on endoscopic ultrasonography (EUS) for retrospectively differentiating pancreatic neuroendocrine tumors (PNETs) from pancreatic cancer. Methods A total of 257 patients, including 151 with pancreatic cancer and 106 with PNETs, were retroactively enrolled after confirmation through pathological examination. These patients were randomized to either the training or test cohort in a ratio of 7:3. Radiomic features were extracted from the intratumoral and peritumoral regions from conventional EUS images. Following this, the radiomic features underwent dimensionality reduction through the utilization of the least absolute shrinkage and selection operator (LASSO) algorithm. Six machine learning algorithms were utilized to train prediction models employing features with nonzero coefficients. The optimum intratumoral radiomic model was identified and subsequently employed for further analysis. Furthermore, a combined radiomic model integrating both intratumoral and peritumoral radiomic features was established and assessed based on the same machine learning algorithm. Finally, a nomogram was constructed, integrating clinical signature and combined radiomics model. Results 107 radiomic features were extracted from EUS and only those with nonzero coefficients were kept. Among the six radiomic models, the support vector machine (SVM) model had the highest performance with AUCs of 0.853 in the training cohort and 0.755 in the test cohort. A peritumoral radiomic model was developed and assessed, achieving an AUC of 0.841 in the training and 0.785 in the test cohorts. The amalgamated model, incorporating intratumoral and peritumoral radiomic features, exhibited superior predictive accuracy in both the training (AUC=0.861) and test (AUC=0.822) cohorts. These findings were validated using the Delong test. The calibration and decision curve analyses (DCA) of the combined radiomic model displayed exceptional accuracy and provided the greatest net benefit for clinical decision-making when compared to other models. Finally, the nomogram also achieved an excellent performance. Conclusions An efficient and accurate EUS-based radiomic model incorporating intratumoral and peritumoral radiomic features was proposed and validated to accurately distinguish PNETs from pancreatic cancer. This research has the potential to offer novel perspectives on enhancing the clinical utility of EUS in the prediction of PNETs.
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Affiliation(s)
- Shuangyang Mo
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Nan Yi
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fengyan Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Huaying Zhao
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yingwei Wang
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haiyan Qin
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haixiao Wei
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haixing Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Cui XW, Goudie A, Blaivas M, Chai YJ, Chammas MC, Dong Y, Stewart J, Jiang TA, Liang P, Sehgal CM, Wu XL, Hsieh PCC, Adrian S, Dietrich CF. WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2025; 51:428-438. [PMID: 39672681 DOI: 10.1016/j.ultrasmedbio.2024.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 10/24/2024] [Accepted: 10/31/2024] [Indexed: 12/15/2024]
Abstract
Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally associated with human intelligence. At present, AI has been widely used in a variety of ultrasound tasks, including in point-of-care ultrasound, echocardiography, and various diseases of different organs. However, the characteristics of ultrasound, compared to other imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses significant additional challenges to AI. Application of AI can not only reduce variability during ultrasound image acquisition, but can standardize these interpretations and identify patterns that escape the human eye and brain. These advances have enabled greater innovations in ultrasound AI applications that can be applied to a variety of clinical settings and disease states. Therefore, The World Federation of Ultrasound in Medicine and Biology (WFUMB) is addressing the topic with a brief and practical overview of current and potential future AI applications in medical ultrasound, as well as discuss some current limitations and future challenges to AI implementation.
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Affiliation(s)
- Xin Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College and State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Adrian Goudie
- Department of Emergency, Fiona Stanley Hospital, Perth, Australia
| | - Michael Blaivas
- Department of Medicine, University of South Carolina School of Medicine, Columbia, SC, USA
| | - Young Jun Chai
- Department of Surgery, Seoul National University College of Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Maria Cristina Chammas
- Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Yi Dong
- Department of Ultrasound, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jonathon Stewart
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
| | - Tian-An Jiang
- Department of Ultrasound Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ping Liang
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China
| | - Chandra M Sehgal
- Ultrasound Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Xing-Long Wu
- School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, Hubei, China
| | | | - Saftoiu Adrian
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Christoph F Dietrich
- Department General Internal Medicine (DAIM), Hospitals Hirslanden Bern Beau Site, Salem and Permanence, Bern, Switzerland.
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Podină N, Gheorghe EC, Constantin A, Cazacu I, Croitoru V, Gheorghe C, Balaban DV, Jinga M, Țieranu CG, Săftoiu A. Artificial Intelligence in Pancreatic Imaging: A Systematic Review. United European Gastroenterol J 2025; 13:55-77. [PMID: 39865461 PMCID: PMC11866320 DOI: 10.1002/ueg2.12723] [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: 07/11/2024] [Revised: 10/24/2024] [Accepted: 11/03/2024] [Indexed: 01/28/2025] Open
Abstract
The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.
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Affiliation(s)
- Nicoleta Podină
- “Carol Davila” University of Medicine and PharmacyBucharestRomania
- Department of GastroenterologyPonderas Academic HospitalBucharestRomania
| | | | - Alina Constantin
- Department of GastroenterologyPonderas Academic HospitalBucharestRomania
| | - Irina Cazacu
- Oncology DepartmentFundeni Clinical InstituteBucharestRomania
| | - Vlad Croitoru
- Oncology DepartmentFundeni Clinical InstituteBucharestRomania
| | - Cristian Gheorghe
- “Carol Davila” University of Medicine and PharmacyBucharestRomania
- Center of Gastroenterology and HepatologyFundeni Clinical InstituteBucharestRomania
| | - Daniel Vasile Balaban
- “Carol Davila” University of Medicine and PharmacyBucharestRomania
- Department of Gastroenterology“Carol Davila” Central Military University Emergency HospitalBucharestRomania
| | - Mariana Jinga
- “Carol Davila” University of Medicine and PharmacyBucharestRomania
- Department of Gastroenterology“Carol Davila” Central Military University Emergency HospitalBucharestRomania
| | - Cristian George Țieranu
- “Carol Davila” University of Medicine and PharmacyBucharestRomania
- Department of Gastroenterology and HepatologyElias Emergency University HospitalBucharestRomania
| | - Adrian Săftoiu
- “Carol Davila” University of Medicine and PharmacyBucharestRomania
- Department of GastroenterologyPonderas Academic HospitalBucharestRomania
- Department of Gastroenterology and HepatologyElias Emergency University HospitalBucharestRomania
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10
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Wang Z, Lin K, Zheng M, Gong L, Chen Z, Wu M. Accurate measurement of key structures in CBD patients using deep learning. Biomed Signal Process Control 2025; 100:106979. [DOI: 10.1016/j.bspc.2024.106979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]
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11
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Konikoff T, Loebl N, Benson AA, Green O, Sandler H, Gingold-Belfer R, Levi Z, Perl L, Dotan I, Shamah S. Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer-aided detection systems. J Gastroenterol Hepatol 2025; 40:235-240. [PMID: 39538430 DOI: 10.1111/jgh.16814] [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: 05/27/2024] [Revised: 10/24/2024] [Accepted: 10/31/2024] [Indexed: 11/16/2024]
Abstract
BACKGROUND AND AIM Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real-time detection and segmentation of all pancreatic lesions. METHODS In this single-center study EUS studies of pancreatic findings were included. Lesions were outlined by two expert (>5 years performing EUS) endoscopists, and the two leading types of models were benchmarked. The models' performance was evaluated through per-pixel intersection over union (IoU). RESULTS A total of 1497 EUS images from 165 patients were evaluated. The dataset included malignancies, neuroendocrine tumors, benign cysts, chronic and acute pancreatitis, normal fatty pancreas, and benign lesions. The best model demonstrated detection and segmentation on the test set with a mean IoU of 0.73, achieving a PPV, NPV, total accuracy, and ROC of 0.82, 0.96, 0.95, and 0.95, respectively. The algorithm is adaptable for real-time processing. CONCLUSIONS We developed and tested deep learning models for real-time detection and segmentation of pancreatic lesions during EUS with promising results. This constitutes the basis for a CADe system for EUS, which could be valuable in future detection and evaluation of pancreatic lesions. Further studies for validation and generalization are underway.
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Affiliation(s)
- Tom Konikoff
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Nadav Loebl
- Rabin Medical Center Innovation Lab, Rabin Medical Center, Petah Tikva, Israel
| | - Ariel A Benson
- Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Institute of Gastroenterology and Liver Diseases, Jerusalem, Israel
| | - Orr Green
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Hunter Sandler
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Rachel Gingold-Belfer
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Zohar Levi
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Leor Perl
- Rabin Medical Center Innovation Lab, Rabin Medical Center, Petah Tikva, Israel
| | - Iris Dotan
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Steven Shamah
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
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12
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Corallo C, Al-Adhami AS, Jamieson N, Valle J, Radhakrishna G, Moir J, Albazaz R. An update on pancreatic cancer imaging, staging, and use of the PACT-UK radiology template pre- and post-neoadjuvant treatment. Br J Radiol 2025; 98:13-26. [PMID: 39460945 DOI: 10.1093/bjr/tqae217] [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: 12/12/2023] [Revised: 10/01/2024] [Accepted: 10/22/2024] [Indexed: 10/28/2024] Open
Abstract
Pancreatic ductal adenocarcinoma continues to have a poor prognosis, although recent advances in neoadjuvant treatments (NATs) have provided some hope. Imaging assessment of suspected tumours can be challenging and requires a specific approach, with pancreas protocol CT being the primary imaging modality for staging with other modalities used as problem-solving tools to facilitate appropriate management. Imaging assessment post NAT can be particularly difficult due to a current lack of robust radiological criteria to predict response and differentiate treatment induced fibrosis/inflammation from residual tumour. This review aims to provide an update of pancreatic ductal adenocarcinoma with particular focus on three points: tumour staging pre- and post-NAT including vascular assessment, structured reporting with introduction of the PAncreatic Cancer reporting Template-UK (PACT-UK) radiology template, and the potential future role of artificial intelligence in the diagnosis and staging of pancreatic cancer.
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Affiliation(s)
- Carmelo Corallo
- Department of Radiology, St James's University Hospital, Leeds LS9 7TF, United Kingdom
| | - Abdullah S Al-Adhami
- Department of Radiology, Glasgow Royal Infirmary, Glasgow G31 2ER, United Kingdom
| | - Nigel Jamieson
- HPB Unit, Glasgow Royal Infirmary, Glasgow G31 2ER, United Kingdom
| | - Juan Valle
- Division of Cancer Sciences, University of Manchester, Manchester M20 4GJ, United Kingdom
- Department of Medical Oncology, The Christie NHS Foundation Trust, Manchester M20 4 BX, United Kingdom
| | | | - John Moir
- HPB Unit, Freeman Hospital, Newcastle Upon Tyne NE7 7DN, United Kingdom
| | - Raneem Albazaz
- Department of Radiology, St James's University Hospital, Leeds LS9 7TF, United Kingdom
- University of Leeds, Leeds LS2 9JT, United Kingdom
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13
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Lin CK, Wu SH, Chua YW, Fan HJ, Cheng YC. TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions. J Formos Med Assoc 2025; 124:28-37. [PMID: 38702216 DOI: 10.1016/j.jfma.2024.04.016] [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: 08/28/2023] [Revised: 03/14/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024] Open
Abstract
The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain multiple imaging modes. Different imaging modes and different frames can reflect the different characteristics of lesions. Compared with previous studies, the proposed model can efficiently extract and integrate the spatiotemporal relationships between different modes and does not require manual selection of representative frames. In recent years, Vision Transformer has received much attention in the field of computer vision. Combined with convolutional neural networks, hybrid transformers can also perform well on small datasets. This study designed a novel deep learning architecture based on hybrid transformer called TransEBUS. By adding learnable parameters in the temporal dimension, TransEBUS was able to extract spatiotemporal features from insufficient data. In addition, we designed a two-stream module to integrate information from three different imaging modes of EBUS. Furthermore, we applied contrastive learning when training TransEBUS, enabling it to learn discriminative representation of benign and malignant mediastinal lesions. The results show that TransEBUS achieved a diagnostic accuracy of 82% and an area under the curve of 0.8812 in the test dataset, outperforming other methods. It also shows that several models can improve performance by incorporating two-stream module. Our proposed system has shown its potential to help physicians distinguishing benign and malignant mediastinal lesions, thereby ensuring the accuracy of EBUS examination.
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Affiliation(s)
- Ching-Kai Lin
- Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan
| | - Shao-Hua Wu
- Department of Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan.
| | - Yi-Wei Chua
- Department of Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan
| | - Hung-Jen Fan
- Department of Medicine, National Taiwan University Cancer Center, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Biomedical Park Hospital, Hsin-Chu County, 302, Taiwan
| | - Yun-Chien Cheng
- Department of Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan.
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14
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Seyithanoglu D, Durak G, Keles E, Medetalibeyoglu A, Hong Z, Zhang Z, Taktak YB, Cebeci T, Tiwari P, Velichko YS, Yazici C, Tirkes T, Miller FH, Keswani RN, Spampinato C, Wallace MB, Bagci U. Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations. Cancers (Basel) 2024; 16:4268. [PMID: 39766167 PMCID: PMC11674829 DOI: 10.3390/cancers16244268] [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: 10/24/2024] [Revised: 12/08/2024] [Accepted: 12/19/2024] [Indexed: 01/11/2025] Open
Abstract
Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.
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Affiliation(s)
- Deniz Seyithanoglu
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Gorkem Durak
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Elif Keles
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Alpay Medetalibeyoglu
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Ziliang Hong
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Zheyuan Zhang
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Yavuz B. Taktak
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Timurhan Cebeci
- Istanbul Faculty of Medicine, Istanbul University, Istanbul 38000, Turkey; (Y.B.T.); (T.C.)
| | - Pallavi Tiwari
- Department of Radiology, BME, University of Wisconsin-Madison, Madison, WI 53707, USA;
- William S. Middleton Memorial Veterans Affairs (VA) Healthcare, 2500 Overlook Terrace, Madison, WI 53705, USA
| | - Yuri S. Velichko
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Cemal Yazici
- Department of Gastroenterology, University of Illinois at Chicago, Chicago, IL 60611, USA;
| | - Temel Tirkes
- Department of Radiology, Indiana University, Indianapolis, IN 46202, USA;
| | - Frank H. Miller
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Rajesh N. Keswani
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
| | - Concetto Spampinato
- Department of Electrical, Electronics and Computer Engineering, University of Catania, 95124 Catania, Italy;
| | - Michael B. Wallace
- Department of Gastroenterology, Mayo Clinic Florida, Jacksonville, FL 32224, USA;
| | - Ulas Bagci
- Machine and Hybrid Intelligence Lab, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; (D.S.); (G.D.); (E.K.); (A.M.); (Z.H.); (Z.Z.); (Y.S.V.); (F.H.M.); (R.N.K.)
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15
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Wu Y, Ramai D, Smith ER, Mega PF, Qatomah A, Spadaccini M, Maida M, Papaefthymiou A. Applications of Artificial Intelligence in Gastrointestinal Endoscopic Ultrasound: Current Developments, Limitations and Future Directions. Cancers (Basel) 2024; 16:4196. [PMID: 39766095 PMCID: PMC11674484 DOI: 10.3390/cancers16244196] [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: 11/03/2024] [Revised: 12/09/2024] [Accepted: 12/14/2024] [Indexed: 01/09/2025] Open
Abstract
Endoscopic ultrasound (EUS) effectively diagnoses malignant and pre-malignant gastrointestinal lesions. In the past few years, artificial intelligence (AI) has shown promising results in enhancing EUS sensitivity and accuracy, particularly for subepithelial lesions (SELs) like gastrointestinal stromal tumors (GISTs). Furthermore, AI models have shown high accuracy in predicting malignancy in gastric GISTs and distinguishing between benign and malignant intraductal papillary mucinous neoplasms (IPMNs). The utility of AI has also been applied to existing and emerging technologies involved in the performance and evaluation of EUS-guided biopsies. These advancements may improve training in EUS, allowing trainees to focus on technical skills and image interpretation. This review evaluates the current state of AI in EUS, covering imaging diagnosis, EUS-guided biopsies, and training advancements. It discusses early feasibility studies and recent developments, while also addressing the limitations and challenges. This article aims to review AI applications to EUS and its applications in clinical practice while addressing pitfalls and challenges.
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Affiliation(s)
- Yizhong Wu
- Department of Internal Medicine, Baylor Scott & White Round Rock Hospital, Round Rock, TX 78665, USA;
| | - Daryl Ramai
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Eric R. Smith
- Department of Internal Medicine, Baylor Scott & White Round Rock Hospital, Round Rock, TX 78665, USA;
| | - Paulo F. Mega
- Gastrointestinal Endoscopy Unit, Universidade de Sao Paulo Hospital das Clinicas, São Paulo 05403-010, Brazil
| | - Abdulrahman Qatomah
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Marcello Maida
- Department of Medicine and Surgery, School of Medicine and Surgery, University of Enna ‘Kore’, 94100 Enna, Italy;
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16
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Noort FVD, Borg FT, Guitink A, Faber J, Wolterink JM. Deep learning for segmentation of colorectal carcinomas on endoscopic ultrasound. Tech Coloproctol 2024; 29:20. [PMID: 39671056 DOI: 10.1007/s10151-024-03056-5] [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: 04/23/2024] [Accepted: 11/06/2024] [Indexed: 12/14/2024]
Abstract
BACKGROUND Bowel-preserving local resection of early rectal cancer is less successful if the tumor infiltrates the muscularis propria as opposed to submucosal infiltration only. Magnetic resonance imaging currently lacks the spatial resolution to provide a reliable estimation of the infiltration depth. Endoscopic ultrasound (EUS) has better resolution, but its interpretation is investigator dependent. We hypothesize that automated image segmentation of EUS could be a way to standardize EUS interpretation. METHODS EUS media and outcome data were collected prospectively. Based on 373 expert manual segmentations, a convolutional neural network was developed to perform segmentation of the submucosa, muscularis propria, and tumors. The mean surface distance (MSD), maximal distance between segmentations (Hausdorff distance; HDD), and overlap (Dice similarity index; DSI) were calculated. RESULTS The median MSD and HDD values were 3.2 and 17.7 pixels for the tumor, 3.4 and 24.7 pixels for the submucosa, and 2.6 and 20.0 pixels for the muscularis propria, respectively. The median DSI values for the tumor, submucosa, and muscularis propria were 0.82, 0.57, and 0.59, respectively. These values reflect good agreement between manual and deep learning segmentation. CONCLUSIONS This study found encouraging results of using automated analysis of EUS images of early rectal cancer, supporting further exploration in clinical practice.
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Affiliation(s)
- F van den Noort
- Department of Applied Mathematics, Technical Medical Center, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands.
| | - F Ter Borg
- Department of Gastroenterology & Hepatology, Deventer Hospital, Deventer, the Netherlands
| | - A Guitink
- Department of Gastroenterology & Hepatology, Deventer Hospital, Deventer, the Netherlands
| | - J Faber
- Department of Epidemiology, Deventer Hospital, Deventer, the Netherlands
| | - J M Wolterink
- Department of Applied Mathematics, Technical Medical Center, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands
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Labaki C, Uche-Anya EN, Berzin TM. Artificial Intelligence in Gastrointestinal Endoscopy. Gastroenterol Clin North Am 2024; 53:773-786. [PMID: 39489586 DOI: 10.1016/j.gtc.2024.08.005] [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: 11/05/2024]
Abstract
Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.
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Affiliation(s)
- Chris Labaki
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 300 Brookline Avenue, Boston, MA, USA
| | - Eugenia N Uche-Anya
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, USA.
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18
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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Agudo Castillo B, Mascarenhas M, Martins M, Mendes F, de la Iglesia D, Costa AMMPD, Esteban Fernández-Zarza C, González-Haba Ruiz M. Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2024; 116:613-622. [PMID: 38832589 DOI: 10.17235/reed.2024.10456/2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.
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Affiliation(s)
| | | | - Miguel Martins
- Gastroenterology, Centro Hospitalar Universitário de São João
| | - Francisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Portugal
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20
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Udriștoiu AL, Podină N, Ungureanu BS, Constantin A, Georgescu CV, Bejinariu N, Pirici D, Burtea DE, Gruionu L, Udriștoiu S, Săftoiu A. Deep learning segmentation architectures for automatic detection of pancreatic ductal adenocarcinoma in EUS-guided fine-needle biopsy samples based on whole-slide imaging. Endosc Ultrasound 2024; 13:335-344. [PMID: 39802107 PMCID: PMC11723688 DOI: 10.1097/eus.0000000000000094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 10/27/2024] [Indexed: 01/16/2025] Open
Abstract
Background EUS-guided fine-needle biopsy is the procedure of choice for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). Nevertheless, the samples obtained are small and require expertise in pathology, whereas the diagnosis is difficult in view of the scarcity of malignant cells and the important desmoplastic reaction of these tumors. With the help of artificial intelligence, the deep learning architectures produce a fast, accurate, and automated approach for PDAC image segmentation based on whole-slide imaging. Given the effectiveness of U-Net in semantic segmentation, numerous variants and improvements have emerged, specifically for whole-slide imaging segmentation. Methods In this study, a comparison of 7 U-Net architecture variants was performed on 2 different datasets of EUS-guided fine-needle biopsy samples from 2 medical centers (31 and 33 whole-slide images, respectively) with different parameters and acquisition tools. The U-Net architecture variants evaluated included some that had not been previously explored for PDAC whole-slide image segmentation. The evaluation of their performance involved calculating accuracy through the mean Dice coefficient and mean intersection over union (IoU). Results The highest segmentation accuracies were obtained using Inception U-Net architecture for both datasets. PDAC tissue was segmented with the overall average Dice coefficient of 97.82% and IoU of 0.87 for Dataset 1, respectively, overall average Dice coefficient of 95.70%, and IoU of 0.79 for Dataset 2. Also, we considered the external testing of the trained segmentation models by performing the cross evaluations between the 2 datasets. The Inception U-Net model trained on Train Dataset 1 performed with the overall average Dice coefficient of 93.12% and IoU of 0.74 on Test Dataset 2. The Inception U-Net model trained on Train Dataset 2 performed with the overall average Dice coefficient of 92.09% and IoU of 0.81 on Test Dataset 1. Conclusions The findings of this study demonstrated the feasibility of utilizing artificial intelligence for assessing PDAC segmentation in whole-slide imaging, supported by promising scores.
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Affiliation(s)
| | - Nicoleta Podină
- Department of Gastroenterology, Ponderas Academic Hospital, Bucharest, Romania
- Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Bogdan Silviu Ungureanu
- Department of Gastroenterology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy Craiova, Craiova, Romania
| | - Alina Constantin
- Department of Gastroenterology, Ponderas Academic Hospital, Bucharest, Romania
| | | | - Nona Bejinariu
- REGINA MARIA Regional Laboratory, Pathological Anatomy Division, Cluj-Napoca, Romania
| | - Daniel Pirici
- Department of Histology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Daniela Elena Burtea
- Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy Craiova, Craiova, Romania
| | - Lucian Gruionu
- Faculty of Mechanics, University of Craiova, Craiova, Romania
| | - Stefan Udriștoiu
- Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania
| | - Adrian Săftoiu
- Department of Gastroenterology, Ponderas Academic Hospital, Bucharest, Romania
- Department of Gastroenterology and Hepatology, Elias University Emergency Hospital, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
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Daum N, Blaivas M, Goudie A, Hoffmann B, Jenssen C, Neubauer R, Recker F, Moga TV, Zervides C, Dietrich CF. Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine. Ultrasound J 2024; 16:44. [PMID: 39331224 PMCID: PMC11436506 DOI: 10.1186/s13089-024-00382-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 06/11/2024] [Indexed: 09/28/2024] Open
Abstract
The digitization of medicine will play an increasingly significant role in future years. In particular, telemedicine, Virtual Reality (VR) and innovative Artificial Intelligence (AI) systems offer tremendous potential in imaging diagnostics and are expected to shape ultrasound diagnostics and teaching significantly. However, it is crucial to consider the advantages and disadvantages of employing these new technologies and how best to teach and manage their use. This paper provides an overview of telemedicine, VR and AI in student ultrasound education, presenting current perspectives and controversies.
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Affiliation(s)
- Nils Daum
- Department of Anesthesiology and Intensive Care Medicine (CCM/CVK), Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- Brandenburg Institute for Clinical Ultrasound (BICUS) at Brandenburg Medical University, Neuruppin, Germany
| | - Michael Blaivas
- Department of Medicine, University of South Carolina School of Medicine, Columbia, SC, USA
| | | | - Beatrice Hoffmann
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Christian Jenssen
- Brandenburg Institute for Clinical Ultrasound (BICUS) at Brandenburg Medical University, Neuruppin, Germany
- Department for Internal Medicine, Krankenhaus Märkisch Oderland, Strausberg, Germany
| | | | - Florian Recker
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - Tudor Voicu Moga
- Department of Gastroenterology and Hepatology, "Victor Babeș" University of Medicine and Pharmacy, Piața Eftimie Murgu 2, 300041, Timișoara, Romania
- Center of Advanced Research in Gastroenterology and Hepatology, "Victor Babeș" University of Medicine and Pharmacy, 300041, Timisoara, Romania
| | | | - Christoph Frank Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Hirslanden Beau Site, Salem und Permanence, Bern, Switzerland.
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22
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Jiang H, Ye LS, Yuan XL, Luo Q, Zhou NY, Hu B. Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions. J Dig Dis 2024; 25:564-572. [PMID: 39740251 DOI: 10.1111/1751-2980.13324] [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: 09/15/2024] [Revised: 11/13/2024] [Accepted: 12/03/2024] [Indexed: 01/02/2025]
Abstract
Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical needs, which presents challenges such as significant difficulty in operation and risks of missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has enhanced the diagnostic and treatment efficiency and quality of pancreaticobiliary endoscopy. Diagnosis and differential diagnosis based on endoscopic ultrasound (EUS) images, pathology of EUS-guided fine-needle aspiration or biopsy, need for endoscopic retrograde cholangiopancreatography (ERCP) and assessment of operational difficulty, postoperative complications and prediction of patient prognosis, and real-time procedure guidance. This review provides an overview of AI applications in pancreaticobiliary endoscopy and proposes future development directions in aspects such as data quality and algorithmic interpretability, aiming to provide new insights for the integration of AI technology with pancreaticobiliary endoscopy.
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Affiliation(s)
- Huan Jiang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Lian Song Ye
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xiang Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Qi Luo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Nuo Ya Zhou
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Med-X Center for Materials, Sichuan University, Chengdu, Sichuan Province, China
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23
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Li J, Zhang P, Yang X, Zhu L, Wang T, Zhang P, Liu R, Sheng B, Wang K. SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images. Artif Intell Med 2024; 154:102919. [PMID: 38941908 DOI: 10.1016/j.artmed.2024.102919] [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: 11/14/2023] [Revised: 04/30/2024] [Accepted: 06/19/2024] [Indexed: 06/30/2024]
Abstract
Pancreatic cancer does not show specific symptoms, which makes the diagnosis of early stages difficult with established image-based screening methods and therefore has the worst prognosis among all cancers. Although endoscopic ultrasonography (EUS) has a key role in diagnostic algorithms for pancreatic diseases, B-mode imaging of the pancreas can be affected by confounders such as chronic pancreatitis, which can make both pancreatic lesion segmentation and classification laborious and highly specialized. To address these challenges, this work proposes a semi-supervised multi-task network (SSM-Net) to leverage unlabeled and labeled EUS images for joint pancreatic lesion classification and segmentation. Specifically, we first devise a saliency-aware representation learning module (SRLM) on a large number of unlabeled images to train a feature extraction encoder network for labeled images by computing a contrastive loss with a semantic saliency map, which is obtained by our spectral residual module (SRM). Moreover, for labeled EUS images, we devise channel attention blocks (CABs) to refine the features extracted from the pre-trained encoder on unlabeled images for segmenting lesions, and then devise a merged global attention module (MGAM) and a feature similarity loss (FSL) for obtaining a lesion classification result. We collect a large-scale EUS-based pancreas image dataset (LS-EUSPI) consisting of 9,555 pathologically proven labeled EUS images (499 patients from four categories) and 15,500 unlabeled EUS images. Experimental results on the LS-EUSPI dataset and a public thyroid gland lesion dataset show that our SSM-Net clearly outperforms state-of-the-art methods.
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Affiliation(s)
- Jiajia Li
- School of Chemistry and Chemical Engineering and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Pingping Zhang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
| | - Xia Yang
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shangdong First Medical University, Jinan, Shandong, 250021, China.
| | - Lei Zhu
- Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, China; Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China.
| | - Teng Wang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
| | - Ping Zhang
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA; Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA.
| | - Ruhan Liu
- Furong Laboratory, Central South University, Changsha, Hunan, China; Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Kaixuan Wang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China.
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24
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Liu W, Zhang B, Liu T, Jiang J, Liu Y. Artificial Intelligence in Pancreatic Image Analysis: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:4749. [PMID: 39066145 PMCID: PMC11280964 DOI: 10.3390/s24144749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.
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Affiliation(s)
- Weixuan Liu
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Bairui Zhang
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Tao Liu
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;
| | - Juntao Jiang
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yong Liu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
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Rousta F, Esteki A, Shalbaf A, Sadeghi A, Moghadam PK, Voshagh A. Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108205. [PMID: 38703435 DOI: 10.1016/j.cmpb.2024.108205] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/13/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024]
Abstract
The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.
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Affiliation(s)
- Fatemeh Rousta
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Esteki
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Sadeghi
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Pardis Ketabi Moghadam
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ardalan Voshagh
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
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26
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Mo S, Huang C, Wang Y, Zhao H, Wei H, Qin H, Jiang H, Qin S. Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer. Front Oncol 2024; 14:1359364. [PMID: 38854733 PMCID: PMC11158619 DOI: 10.3389/fonc.2024.1359364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/06/2024] [Indexed: 06/11/2024] Open
Abstract
OBJECTIVES To develop and validate various ultrasomics models based on endoscopic ultrasonography (EUS) for retrospective differentiating pancreatic neuroendocrine tumors (PNET) from pancreatic cancer. METHODS A total of 231 patients, comprising 127 with pancreatic cancer and 104 with PNET, were retrospectively enrolled. These patients were randomly divided into either a training or test cohort at a ratio of 7:3. Ultrasomics features were extracted from conventional EUS images, focusing on delineating the region of interest (ROI) for pancreatic lesions. Subsequently, dimensionality reduction of the ultrasomics features was performed by applying the Mann-Whitney test and least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning algorithms, namely logistic regression (LR), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), random forest (RF), extra trees, k nearest neighbors (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to train prediction models using nonzero coefficient features. The optimal ultrasomics model was determined using a ROC curve and utilized for subsequent analysis. Clinical-ultrasonic features were assessed using both univariate and multivariate logistic regression. An ultrasomics nomogram model, integrating both ultrasomics and clinical-ultrasonic features, was developed. RESULTS A total of 107 EUS-based ultrasomics features were extracted, and 6 features with nonzero coefficients were ultimately retained. Among the eight ultrasomics models based on machine learning algorithms, the RF model exhibited superior performance with an AUC= 0.999 (95% CI 0.9977 - 1.0000) in the training cohort and an AUC= 0.649 (95% CI 0.5215 - 0.7760) in the test cohort. A clinical-ultrasonic model was established and evaluated, yielding an AUC of 0.999 (95% CI 0.9961 - 1.0000) in the training cohort and 0.847 (95% CI 0.7543 - 0.9391) in the test cohort. Subsequently, the ultrasomics nomogram demonstrated a significant improvement in prediction accuracy in the test cohort, as evidenced by an AUC of 0.884 (95% CI 0.8047 - 0.9635) and confirmed by the Delong test. The calibration curve and decision curve analysis (DCA) depicted this ultrasomics nomogram demonstrated superior accuracy. They also yielded the highest net benefit for clinical decision-making compared to alternative models. CONCLUSIONS A novel ultrasomics nomogram was proposed and validated, that integrated clinical-ultrasonic and ultrasomics features obtained through EUS, aiming to accurately and efficiently identify pancreatic cancer and PNET.
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Affiliation(s)
- Shuangyang Mo
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cheng Huang
- Oncology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yingwei Wang
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Huaying Zhao
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haixiao Wei
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haiyan Qin
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haixing Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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27
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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28
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Tian S, Shi H, Chen W, Li S, Han C, Du F, Wang W, Wen H, Lei Y, Deng L, Tang J, Zhang J, Lin J, Shi L, Ning B, Zhao K, Miao J, Wang G, Hou H, Huang X, Kong W, Jin X, Ding Z, Lin R. Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study. Int J Surg 2024; 110:1637-1644. [PMID: 38079604 PMCID: PMC10942157 DOI: 10.1097/js9.0000000000000995] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/27/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images. METHODS The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared. RESULTS In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98). CONCLUSIONS The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.
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Affiliation(s)
- Shuxin Tian
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
- Department of Gastroenterology, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi
- National Health Commission Key Laboratory of Central Asia High Incidence Disease Prevention and Control, Shihezi
| | - Huiying Shi
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Weigang Chen
- Department of Gastroenterology, The First Affiliated Hospital of Medical College, Shihezi University, Shihezi
- National Health Commission Key Laboratory of Central Asia High Incidence Disease Prevention and Control, Shihezi
| | - Shijie Li
- National Health Commission Key Laboratory of Central Asia High Incidence Disease Prevention and Control, Shihezi
- Department of Endoscopy Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing
| | - Chaoqun Han
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Fan Du
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Weijun Wang
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Hongxu Wen
- Department of Gastroenterology, Lanzhou Second People’s Hospital, Lanzhou
| | - Yali Lei
- Department of Gastroenterology, Weinan Central Hospital, Weinan
| | - Liang Deng
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing
| | - Jing Tang
- Department of Gastroenterology, Fuling Hospital Affiliated to Chongqing University, Chongqing
| | - Jinjie Zhang
- Department of Gastroenterology, The Second Affiliated Hospital of Baotou Medical College, Baotou
| | - Jianjiao Lin
- Department of Gastroenterology, Longgang District People’s Hospital, Shenzhen
| | - Lei Shi
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou
| | - Bo Ning
- Department of Gastroenterology, The Second Affiliated Hospital Chongqing Medical University, Chongqing
| | - Kui Zhao
- Department of Gastroenterology, The First Affiliated Hospital of Chendu Medical College, Chengdu
| | - Jiarong Miao
- Department of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming
- Yunnan Province Clinical Research Center for Digestive Diseases, Kunming
| | - Guobao Wang
- Department of endoscopy, Sun Yat-sen University Cancer Center,Guangzhou
| | - Hui Hou
- Department of Gastroenterology, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi
| | - Xiaoxi Huang
- Department of Gastroenterology, Haikou People’s Hospital, Haikou
| | - Wenjie Kong
- Department of Gastroenterology, People’s Hospital of Xinjiang Autonomous Region, Urumqi
| | - Xiaojuan Jin
- Department of Gastroenterology, Suining Central Hospital, Suining, People’s Republic of China
| | - Zhen Ding
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
- Department of Endoscopy Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Rong Lin
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
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29
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Badaoui A, Teles de Campos S, Fusaroli P, Gincul R, Kahaleh M, Poley JW, Sosa Valencia L, Czako L, Gines A, Hucl T, Kalaitzakis E, Petrone MC, Sadik R, van Driel L, Vandeputte L, Tham T. Curriculum for diagnostic endoscopic ultrasound training in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2024; 56:222-240. [PMID: 38065561 DOI: 10.1055/a-2224-8704] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The European Society of Gastrointestinal Endoscopy (ESGE) has recognized the need to formalize and enhance training in diagnostic endoscopic ultrasound (EUS). This manuscript represents the outcome of a formal Delphi process resulting in an official Position Statement of the ESGE and provides a framework to develop and maintain skills in diagnostic EUS. This curriculum is set out in terms of the prerequisites prior to training; the recommended steps of training to a defined syllabus; the quality of training; and how competence should be defined and evidenced before independent practice. 1: Trainees should have achieved competence in upper gastrointestinal endoscopy before training in diagnostic EUS. 2: The development of diagnostic EUS skills by methods that do not involve patients is advisable, but not mandatory, prior to commencing formal training in diagnostic EUS. 3: A trainee's principal trainer should be performing adequate volumes of diagnostic EUSs to demonstrate maintenance of their own competence. 4: Training centers for diagnostic EUS should offer expertise, as well as a high volume of procedures per year, to ensure an optimal level of quality for training. Under these conditions, training centers should be able to provide trainees with a sufficient wealth of experience in diagnostic EUS for at least 12 months. 5: Trainees should engage in formal training and supplement this with a range of learning resources for diagnostic EUS, including EUS-guided fine-needle aspiration and biopsy (FNA/FNB). 6: EUS training should follow a structured syllabus to guide the learning program. 7: A minimum procedure volume should be offered to trainees during diagnostic EUS training to ensure that they have the opportunity to achieve competence in the technique. To evaluate competence in diagnostic EUS, trainees should have completed a minimum of 250 supervised EUS procedures: 80 for luminal tumors, 20 for subepithelial lesions, and 150 for pancreaticobiliary lesions. At least 75 EUS-FNA/FNBs should be performed, including mostly pancreaticobiliary lesions. 8: Competence assessment in diagnostic EUS should take into consideration not only technical skills, but also cognitive and integrative skills. A reliable valid assessment tool should be used regularly during diagnostic EUS training to track the acquisition of competence and to support trainee feedback. 9: A period of supervised practice should follow the start of independent activity. Supervision can be delivered either on site if other colleagues are already practicing EUS or by maintaining contacts with the training center and/or other EUS experts. 10: Key performance measures including the annual number of procedures, frequency of obtaining a diagnostic sample during EUS-FNA/FNB, and adverse events should be recorded within an electronic documentation system and evaluated.
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Affiliation(s)
- Abdenor Badaoui
- Department of Gastroenterology and Hepatology, CHU UCL NAMUR, Université catholique de Louvain, Yvoir, Belgium
| | - Sara Teles de Campos
- Department of Gastroenterology, Digestive Unit, Champalimaud Foundation, Lisbon, Portugal
| | - Pietro Fusaroli
- Unit of Gastroenterology, University of Bologna, Hospital of Imola, Imola, Italy
| | - Rodica Gincul
- Department of Gastroenterology, Jean Mermoz Private Hospital, Lyon, France
| | - Michel Kahaleh
- Division of Gastroenterology, Rutgers University, New Brunswick, New Jersey, USA
| | - Jan-Werner Poley
- Department of Gastroenterology and Hepatology, Maastricht UMC+, Maastricht, The Netherlands
| | - Leonardo Sosa Valencia
- IHU Strasbourg - Institute of Image-Guided Surgery - Université de Strasbourg, Strasbourg, France
| | - Laszlo Czako
- Division of Gastroenterology, Department of Medicine, University of Szeged, Szeged, Hungary
| | - Angels Gines
- Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic, IDIBAPS, CIBEREHD, University of Barcelona, Barcelona, Spain
| | - Tomas Hucl
- Department of Gastroenterology and Hepatology, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic
| | - Evangelos Kalaitzakis
- Department of Gastroenterology, University Hospital of Heraklion, University of Crete, Heraklion, Greece
| | - Maria Chiara Petrone
- Division of Pancreatobiliary Endoscopy and Endosonography, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Riadh Sadik
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Lydi van Driel
- Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, The Netherlands
| | - Lieven Vandeputte
- Department of Gastroenterology and Hepatology, AZ Sint-Jan Brugge-Oostende AV, Bruges, Belgium
| | - Tony Tham
- Department of Gastroenterology and Hepatology, Ulster Hospital, Dundonald, Northern Ireland
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Robles-Medranda C, Baquerizo-Burgos J, Puga-Tejada M, Del Valle R, Mendez JC, Egas-Izquierdo M, Arevalo-Mora M, Cunto D, Alcívar-Vasquez J, Pitanga-Lukashok H, Tabacelia D. Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos). Gastrointest Endosc 2024; 99:271-279.e2. [PMID: 37827432 DOI: 10.1016/j.gie.2023.10.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND AND AIMS EUS is a high-skill technique that requires numerous procedures to achieve competence. However, training facilities are limited worldwide. Convolutional neural network (CNN) models have been previously implemented for object detection. We developed 2 EUS-based CNN models for normal anatomic structure recognition during real-time linear- and radial-array EUS evaluations. METHODS The study was performed from February 2020 to June 2022. Consecutive patient videos of linear- and radial-array EUS videos were recorded. Expert endosonographers identified and labeled 20 normal anatomic structures within the videos for training and validation of the CNN models. Initial CNN models (CNNv1) were developed from 45 videos and the improved models (CNNv2) from an additional 102 videos. CNN model performance was compared with that of 2 expert endosonographers. RESULTS CNNv1 used 45,034 linear-array EUS frames and 21,063 radial-array EUS frames. CNNv2 used 148,980 linear-array EUS frames and 128,871 radial-array EUS frames. Linear-array CNNv1 and radial-array CNNv1 achieved a 75.65% and 71.36% mean average precision (mAP) with a total loss of .19 and .18, respectively. Linear-array CNNv2 obtained an 88.7% mAP with a .06 total loss, whereas radial-array CNNv2 achieved an 83.5% mAP with a .07 total loss. CNNv2 accurately detected all studied normal anatomic structures with a >98% observed agreement during clinical validation. CONCLUSIONS The proposed CNN models accurately recognize the normal anatomic structures in prerecorded videos and real-time EUS. Prospective trials are needed to evaluate the impact of these models on the learning curves of EUS trainees.
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Affiliation(s)
- Carlos Robles-Medranda
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Jorge Baquerizo-Burgos
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Miguel Puga-Tejada
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Raquel Del Valle
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Juan C Mendez
- Research and Development Department, mdconsgroup, Guayaquil, Ecuador
| | - Maria Egas-Izquierdo
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Martha Arevalo-Mora
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Domenica Cunto
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Juan Alcívar-Vasquez
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Hannah Pitanga-Lukashok
- Gastroenterology and Endoscopy Division, Instituto Ecuatoriano de Enfermedades Digestivas, Guayaquil, Ecuador
| | - Daniela Tabacelia
- Gastroenterology and Hepatology, Elias Emergency University Hospital, Bucharest, Romania; Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
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Li J, Zhang P, Wang T, Zhu L, Liu R, Yang X, Wang K, Shen D, Sheng B. DSMT-Net: Dual Self-Supervised Multi-Operator Transformation for Multi-Source Endoscopic Ultrasound Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:64-75. [PMID: 37368810 DOI: 10.1109/tmi.2023.3289859] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Pancreatic cancer has the worst prognosis of all cancers. The clinical application of endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep learning for the classification of EUS images have been hindered by inter-grader variability and labeling capability. One of the key reasons for these difficulties is that EUS images are obtained from multiple sources with varying resolutions, effective regions, and interference signals, making the distribution of the data highly variable and negatively impacting the performance of deep learning models. Additionally, manual labeling of images is time-consuming and requires significant effort, leading to the desire to effectively utilize a large amount of unlabeled data for network training. To address these challenges, this study proposes the Dual Self-supervised Multi-Operator Transformation Network (DSMT-Net) for multi-source EUS diagnosis. The DSMT-Net includes a multi-operator transformation approach to standardize the extraction of regions of interest in EUS images and eliminate irrelevant pixels. Furthermore, a transformer-based dual self-supervised network is designed to integrate unlabeled EUS images for pre-training the representation model, which can be transferred to supervised tasks such as classification, detection, and segmentation. A large-scale EUS-based pancreas image dataset (LEPset) has been collected, including 3,500 pathologically proven labeled EUS images (from pancreatic and non-pancreatic cancers) and 8,000 unlabeled EUS images for model development. The self-supervised method has also been applied to breast cancer diagnosis and was compared to state-of-the-art deep learning models on both datasets. The results demonstrate that the DSMT-Net significantly improves the accuracy of pancreatic and breast cancer diagnosis.
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Wu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health 2023; 5:e812-e820. [PMID: 37775472 DOI: 10.1016/s2589-7500(23)00160-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/16/2023] [Accepted: 08/09/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Endoscopic ultrasonography (EUS) is a key procedure for the diagnosis of biliopancreatic diseases. However, the performance among EUS endoscopists varies greatly and leads to blind spots during the operation, which can impair the health outcomes of patients. We previously developed an artificial intelligence (AI) device that accurately identified EUS standard stations and significantly reduced the difficulty of ultrasonography image interpretation. In this study, we updated the device (named EUS-IREAD) and validated its performance in improving the quality of EUS procedures. METHODS In this single-centre, randomised, controlled trial, we updated EUS-IREAD so it consisted of five learning models to identify eight EUS stations and 24 anatomical structures. The trial was done at the Renmin Hospital of Wuhan University (Wuhan, China) and included patients aged 18 years or older with suspected biliopancreatic (pancreas and biliopancreatic duct) lesions due to clinical symptoms, radiological findings, or laboratory findings, and with a high risk of pancreatic cancer. Patients were randomly assigned (1:1) by a dedicated research assistant using a computer-generated random number series (with a block size of four) to undergo the EUS procedure with or without the assistance of EUS-IREAD. Endoscopists in the EUS-IREAD-assisted group were required to observe all standard stations and anatomical structures according to the prompts by the AI device. Data collectors, the independent data anaylsis team, and patients were masked to group allocation. The primary outcome was the missed scanning rate of standard stations between the two groups, which was assessed in patients who underwent EUS procedure in accordance with the assigned intervention (per protocol). This trial is registered with ClinicalTrials.gov, NCT05457101. FINDINGS Between July 9, 2022, and Feb 28, 2023, 290 patients (mean age 55·93 years [SD 14·06], 152 [52%] male, and 138 [48%] female) were randomly assigned and analysed, including 144 in the EUS-IREAD-assisted group and 146 in the control group. The EUS-IREAD-assisted group had a lower missed scanning rate of stations than the control group (4·5% [SD 0·8] vs 14·3% [1·0], -9·8% [95% CI -12·2 to -7·5]; odds ratio 3·6 [95% Cl 2·6 to 4·9]; p<0·0001). No significant adverse event was found during the study. INTERPRETATION Our study confirms the capability of EUS-IREAD to monitor the blind spots and reduce the missed rate of stations and structures during EUS procedures. The EUS-IREAD has the potential to play an essential part in EUS quality control. FUNDING Innovation Team Project of Health Commission of Hubei Province and College-enterprise Deepening Reform Project of Wuhan University.
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Affiliation(s)
- Hui Ling Wu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Wen Yao
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hui Ying Shi
- Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lian Lian Wu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chen Xia Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Ru Chen
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Tan
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ning Cui
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ji Xiang Zhang
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bing Xiao
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Rong Rong Gong
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhen Ding
- Endoscopy Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Hong Gang Yu
- Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
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Yao L, Zhang C, Xu B, Yi S, Li J, Ding X, Yu H. A deep learning-based system for mediastinum station localization in linear EUS (with video). Endosc Ultrasound 2023; 12:417-423. [PMID: 37969169 PMCID: PMC10631614 DOI: 10.1097/eus.0000000000000011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/12/2023] [Indexed: 11/17/2023] Open
Abstract
Background and Objectives EUS is a crucial diagnostic and therapeutic method for many anatomical regions, especially in the evaluation of mediastinal diseases and related pathologies. Rapidly finding the standard stations is the key to achieving efficient and complete mediastinal EUS imaging. However, it requires substantial technical skills and extensive knowledge of mediastinal anatomy. We constructed a system, named EUS-MPS (EUS-mediastinal position system), for real-time mediastinal EUS station recognition. Methods The standard scanning of mediastinum EUS was divided into 7 stations. There were 33 010 images in mediastinum EUS examination collected to construct a station classification model. Then, we used 151 videos clips for video validation and used 1212 EUS images from 2 other hospitals for external validation. An independent data set containing 230 EUS images was applied for the man-machine contest. We conducted a crossover study to evaluate the effectiveness of this system in reducing the difficulty of mediastinal ultrasound image interpretation. Results For station classification, the model achieved an accuracy of 90.49% in image validation and 83.80% in video validation. At external validation, the models achieved 89.85% accuracy. In the man-machine contest, the model achieved an accuracy of 84.78%, which was comparable to that of expert (83.91%). The accuracy of the trainees' station recognition was significantly improved in the crossover study, with an increase of 13.26% (95% confidence interval, 11.04%-15.48%; P < 0.05). Conclusions This deep learning-based system shows great performance in mediastinum station localization, having the potential to play an important role in shortening the learning curve and establishing standard mediastinal scanning in the future.
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Affiliation(s)
- Liwen Yao
- Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Chenxia Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Bo Xu
- Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China
| | - Shanshan Yi
- Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China
| | - Juan Li
- Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China
| | - Xiangwu Ding
- Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
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Huang J, Fan X, Liu W. Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases. Diagnostics (Basel) 2023; 13:2815. [PMID: 37685350 PMCID: PMC10487217 DOI: 10.3390/diagnostics13172815] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.
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Affiliation(s)
| | | | - Wentian Liu
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; (J.H.); (X.F.)
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Khalaf K, Terrin M, Jovani M, Rizkala T, Spadaccini M, Pawlak KM, Colombo M, Andreozzi M, Fugazza A, Facciorusso A, Grizzi F, Hassan C, Repici A, Carrara S. A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound. J Clin Med 2023; 12:3757. [PMID: 37297953 PMCID: PMC10253269 DOI: 10.3390/jcm12113757] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 05/28/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses located next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training. METHODS AI algorithms can assist in lesion detection and characterization in EUS by analyzing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great potential for tumor identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images. RESULTS AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and insights into disease pathology. CONCLUSIONS The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accuracy, leading to better patient outcomes and to a reduction in repeated procedures in case of non-diagnostic biopsies.
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Affiliation(s)
- Kareem Khalaf
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5S 1A1, Canada; (K.K.); (K.M.P.)
| | - Maria Terrin
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Manol Jovani
- Division of Gastroenterology, Maimonides Medical Center, SUNY Downstate University, Brooklyn, NY 11219, USA;
| | - Tommy Rizkala
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy;
| | - Marco Spadaccini
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Katarzyna M. Pawlak
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5S 1A1, Canada; (K.K.); (K.M.P.)
| | - Matteo Colombo
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Marta Andreozzi
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Alessandro Fugazza
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
| | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy;
| | - Cesare Hassan
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy;
| | - Alessandro Repici
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy;
| | - Silvia Carrara
- Division of Gastroenterology and Digestive Endoscopy, Humanitas Research Hospital IRCCS, Rozzano, 20089 Milan, Italy; (M.T.); (M.S.); (M.C.); (M.A.); (A.F.); (C.H.); (A.R.)
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Ren Y, Zou D, Xu W, Zhao X, Lu W, He X. Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104591] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Poiraud M, Gkolfakis P, Arvanitakis M. Recent Developments in the Field of Endoscopic Ultrasound for Diagnosis, Staging, and Treatment of Pancreatic Lesions. Cancers (Basel) 2023; 15:cancers15092547. [PMID: 37174012 PMCID: PMC10177103 DOI: 10.3390/cancers15092547] [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: 03/09/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Endoscopic ultrasound (EUS) plays a crucial role in the diagnosis of both solid and cystic pancreatic lesions and in the staging of patients with pancreatic cancer through its use for tissue and fluid sampling. Additionally, in cases of precancerous lesions, EUS-guided therapy can also be provided. This review aims to describe the most recent developments regarding the role of EUS in the diagnosis and staging of pancreatic lesions. Moreover, complementary EUS imaging modalities, the role of artificial intelligence, new devices, and modalities for tissue acquisition, and techniques for EUS-guided treatment are discussed.
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Affiliation(s)
- Marie Poiraud
- Department of Gastroenterology, CUB Erasme Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Paraskevas Gkolfakis
- Department of Gastroenterology, CUB Erasme Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Marianna Arvanitakis
- Department of Gastroenterology, CUB Erasme Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium
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Yin M, Liu L, Gao J, Lin J, Qu S, Xu W, Liu X, Xu C, Zhu J. Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review. Int J Med Inform 2023; 174:105044. [PMID: 36948061 DOI: 10.1016/j.ijmedinf.2023.105044] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Shuting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Wei Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Kuraishi Y, Fumihara D, Yanaidani T, Ishikawa S, Yasuda T, Yamada M, Onishi S, Yamada K, Tanaka T, Tajika M, Niwa Y, Yamaguchi R, Shimizu Y. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy 2023; 55:140-149. [PMID: 35688454 DOI: 10.1055/a-1873-7920] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (pancreatic ductal adenocarcinoma [PDAC], pancreatic adenosquamous carcinoma [PASC], acinar cell carcinoma [ACC], metastatic pancreatic tumor [MPT], neuroendocrine carcinoma [NEC], neuroendocrine tumor [NET], solid pseudopapillary neoplasm [SPN], chronic pancreatitis, and autoimmune pancreatitis [AIP]). METHODS : Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated. RESULTS : 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84-0.97), 0.94 (0.88-0.98), 0.82 (0.68-0.92), and 0.91 (0.85-0.95), respectively. The per-category sensitivities (95 %CI) of each disease were PDAC 0.96 (0.90-0.99), PASC 1.00 (0.05-1.00), ACC 1.00 (0.22-1.00), MPT 0.33 (0.01-0.91), NEC 1.00 (0.22-1.00), NET 0.93 (0.66-1.00), SPN 1.00 (0.22-1.00), chronic pancreatitis 0.78 (0.52-0.94), and AIP 0.73 (0.39-0.94). CONCLUSIONS : Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.
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Affiliation(s)
- Takamichi Kuwahara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Kazuo Hara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Nobumasa Mizuno
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Shin Haba
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Nozomi Okuno
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yasuhiro Kuraishi
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Daiki Fumihara
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Takafumi Yanaidani
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Sho Ishikawa
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Tsukasa Yasuda
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Masanori Yamada
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Sachiyo Onishi
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Keisaku Yamada
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Tsutomu Tanaka
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Masahiro Tajika
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Yasumasa Niwa
- Department of Endoscopy, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Japan
- Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yasuhiro Shimizu
- Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
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40
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Franchellucci G, Andreozzi M, Carrara S, De Luca L, Auriemma F, Paduano D, Calabrese F, Facciorusso A, Poletti V, Zerbi A, Lania AG, Bertuzzi AF, Spaggiari P, Pedicini V, Rodari M, Fusaroli P, Lisotti A, Ofosu A, Repici A, Mangiavillano B. Contrast Enhanced EUS for Predicting Solid Pancreatic Neuroendocrine Tumor Grade and Aggressiveness. Diagnostics (Basel) 2023; 13:239. [PMID: 36673049 PMCID: PMC9857765 DOI: 10.3390/diagnostics13020239] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/29/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Pancreatic neuroendocrine tumor (PNET) behavior assessment is a daily challenge for physicians. Modern PNET management varies from a watch-and-wait strategy to surgery depending on tumor aggressiveness. Therefore, the aggressiveness definition plays a pivotal role in the PNET work-up. The aggressiveness of PNETs is mainly based on the dimensions and histological grading, with sometimes a lack of specificity and sensibility. In the last twenty years, EUS has become a cornerstone in the diagnostic phase of PNET management for its high diagnostic yield and the possibility of obtaining a histological specimen. The number of EUS applications in the PNET work-up has been rapidly increasing with new and powerful possibilities. The application of contrast has led to an important step in PNET detection; in recent years, it has been gaining interesting applications in aggressiveness assessment. In this review, we underline the latest experiences and opportunities in the behavior assessment of PNETs using contact-enhanced EUS and contested enhanced harmonic EUS with a particular focus on the future application and possibility that these techniques could provide.
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Affiliation(s)
- Gianluca Franchellucci
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, 21053 Castellanza, Italy
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy
| | - Marta Andreozzi
- Endoscopic Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
- Department of Clinical Medicine and Surgery, ‘Federico II’ University of Naples, 80131 Naples, Italy
| | - Silvia Carrara
- Endoscopic Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Luca De Luca
- Endoscopic Unit, ASST Santi Paolo e Carlo, 20142 Milan, Italy
| | - Francesco Auriemma
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, 21053 Castellanza, Italy
| | - Danilo Paduano
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, 21053 Castellanza, Italy
| | - Federica Calabrese
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, 21053 Castellanza, Italy
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
| | - Valeria Poletti
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, 21053 Castellanza, Italy
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy
| | - Alessandro Zerbi
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy
- Pancreatic Surgery Unit, Humanitas Clinical and Research Center—IRCCS, 20089 Rozzano, Italy
| | - Andrea Gerardo Lania
- Endocrinology, Diabetology and Medical Andrology Unit, Humanitas Clinical and Research Center—IRCCS, 20089 Rozzano, Italy
| | - Alexia Francesca Bertuzzi
- Medical Oncology and Hematology Unit, Humanitas Clinical and Research Center—IRCCS, 20089 Rozzano, Italy
| | - Paola Spaggiari
- Department of Pathology, Humanitas Clinical and Research Center—IRCCS, 20089 Rozzano, Italy
| | - Vittorio Pedicini
- Department of Interventional Radiology, Humanitas University, Humanitas Research Hospital IRCCS, 20089 Rozzano, Italy
| | - Marcello Rodari
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Rozzano, Italy
| | - Pietro Fusaroli
- Gastroenterology Unit, Hospital of Imola, University of Bologna, 40126 Imola, Italy
| | - Andrea Lisotti
- Gastroenterology Unit, Hospital of Imola, University of Bologna, 40126 Imola, Italy
| | - Andrew Ofosu
- Division of Gastroenterology and Hepatology, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy
- Endoscopic Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
| | - Benedetto Mangiavillano
- Gastrointestinal Endoscopy Unit, Humanitas Mater Domini, 21053 Castellanza, Italy
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Italy
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41
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Tang A, Tian L, Gao K, Liu R, Hu S, Liu J, Xu J, Fu T, Zhang Z, Wang W, Zeng L, Qu W, Dai Y, Hou R, Tang S, Wang X. Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis. Cancer Med 2023; 12:7962-7973. [PMID: 36606571 PMCID: PMC10134340 DOI: 10.1002/cam4.5578] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 10/10/2022] [Accepted: 12/17/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND AIMS Distinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning-based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS-guided fine-needle aspiration (EUS-FNA) in real time. METHODS This is a prospective study. The CH-EUS MASTER system is composed of Model 1 (real-time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH-EUS examinations followed by EUS-FNA were recruited. All patients underwent CH-EUS and were diagnosed both by endoscopists and CH-EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS-FNA with or without CH-EUS MASTER guidance. RESULTS Compared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH-EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH-EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH-EUS MASTER guided EUS-FNA, and were not significantly different compared to the control group. CH-EUS MASTER-guided EUS-FNA significantly improved the first-pass diagnostic yield. CONCLUSION CH-EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time. TRIAL REGISTRATION NUMBER NCT04607720.
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Affiliation(s)
- Anliu Tang
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| | - Li Tian
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| | - Kui Gao
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Rui Liu
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
| | - Shan Hu
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Jinzhu Liu
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Jiahao Xu
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Tian Fu
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Zinan Zhang
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Wujun Wang
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Long Zeng
- Wuhan EndoAngel Medical Technology Co., Ltd., Wuhan, China
| | - Weiming Qu
- Department of Gastroenterology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Yong Dai
- Department of Gastroenterology, The First Affiliated Hospital of University of South China, Hengyang, China
| | - Ruirui Hou
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Ningxia, China
| | - Shoujiang Tang
- Division of Digestive Diseases, Department of Medicine, University of Mississippi Medical Center, Jackson, United States
| | - Xiaoyan Wang
- Department of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Central South University, Changsha, China
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Huang L, Xu Y, Chen J, Liu F, Wu D, Zhou W, Wu L, Pang T, Huang X, Zhang K, Yu H. An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation. Endoscopy 2023; 55:4-11. [PMID: 35554877 DOI: 10.1055/a-1850-6717] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND A computer-assisted (CAD) system was developed to assess, score, and classify the technical difficulty of common bile duct (CBD) stone removal during endoscopic retrograde cholangiopancreatography (ERCP). The efficacy of the CAD system was subsequently assessed through a multicenter, prospective, observational study. METHOD All patients who met the inclusion criteria were included. Based on cholangiogram images, the CAD system analyzed the level of difficulty of stone removal and classified it into "difficult" and "easy" groups. Subsequently, differences in clinical endpoints, including attempts at stone extraction, stone extraction time, total operation time, and stone clearance rates were compared between the two groups. RESULTS 173 patients with CBD stones from three hospitals were included in the study. The group classified as difficult by CAD had more extraction attempts (7.20 vs. 4.20, P < 0.001), more frequent machine lithotripsy (30.4 % vs. 7.1 %, P < 0.001), longer stone extraction time (16.59 vs. 7.69 minutes, P < 0.001), lower single-session stone clearance rate (73.9 % vs. 94.5 %, P < 0.001), and lower total stone clearance rate (89.1 % vs. 97.6 %, P = 0.019) compared with the group classified as easy by CAD. CONCLUSION The CAD system effectively assessed and classified the degree of technical difficulty in endoscopic stone extraction during ERCP. In addition, it automatically provided a quantitative evaluation of CBD and stones, which in turn could help endoscopists to apply suitable procedures and interventional methods to minimize the possible risks associated with endoscopic stone removal.
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Affiliation(s)
- Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jie Chen
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Feng Liu
- Digestive Endoscopy Center, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Deqing Wu
- Digestive Endoscopy Center, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Tingting Pang
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Kuo Zhang
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
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Fleurentin A, Mazellier JP, Meyer A, Montanelli J, Swanstrom L, Gallix B, Sosa Valencia L, Padoy N. Automatic pancreas anatomical part detection in endoscopic ultrasound videos. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2154274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Jean-Paul Mazellier
- IHU, Strasbourg, France
- ICube, University of Strasbourg, CNRS, Strasbourg, France
| | - Adrien Meyer
- ICube, University of Strasbourg, CNRS, Strasbourg, France
| | | | | | | | | | - Nicolas Padoy
- IHU, Strasbourg, France
- ICube, University of Strasbourg, CNRS, Strasbourg, France
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Chen Y, Xu C, Ding W, Sun S, Yue X, Fujita H. Target-aware U-Net with fuzzy skip connections for refined pancreas segmentation. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Seo K, Lim JH, Seo J, Nguon LS, Yoon H, Park JS, Park S. Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach. Cancers (Basel) 2022; 14:5111. [PMID: 36291895 PMCID: PMC9600976 DOI: 10.3390/cancers14205111] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
Endoscopic ultrasonography (EUS) plays an important role in diagnosing pancreatic cancer. Surgical therapy is critical to pancreatic cancer survival and can be planned properly, with the characteristics of the target cancer determined. The physical characteristics of the pancreatic cancer, such as size, location, and shape, can be determined by semantic segmentation of EUS images. This study proposes a deep learning approach for the segmentation of pancreatic cancer in EUS images. EUS images were acquired from 150 patients diagnosed with pancreatic cancer. A network with deep attention features (DAF-Net) is proposed for pancreatic cancer segmentation using EUS images. The performance of the deep learning models (U-Net, Attention U-Net, and DAF-Net) was evaluated by 5-fold cross-validation. For the evaluation metrics, the Dice similarity coefficient (DSC), intersection over union (IoU), receiver operating characteristic (ROC) curve, and area under the curve (AUC) were chosen. Statistical analysis was performed for different stages and locations of the cancer. DAF-Net demonstrated superior segmentation performance for the DSC, IoU, AUC, sensitivity, specificity, and precision with scores of 82.8%, 72.3%, 92.7%, 89.0%, 98.1%, and 85.1%, respectively. The proposed deep learning approach can provide accurate segmentation of pancreatic cancer in EUS images and can effectively assist in the planning of surgical therapies.
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Affiliation(s)
- Kangwon Seo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea
| | - Jung-Hyun Lim
- Division of Gastroenterology, Department of Internal Medicine, Inha University School of Medicine, Incheon 22332, Korea
| | - Jeongwung Seo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea
| | - Leang Sim Nguon
- Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Hongeun Yoon
- Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Jin-Seok Park
- Division of Gastroenterology, Department of Internal Medicine, Inha University School of Medicine, Incheon 22332, Korea
| | - Suhyun Park
- Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760, Korea
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Liu JQ, Ren JY, Xu XL, Xiong LY, Peng YX, Pan XF, Dietrich CF, Cui XW. Ultrasound-based artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2022; 28:5530-5546. [PMID: 36304086 PMCID: PMC9594013 DOI: 10.3748/wjg.v28.i38.5530] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/12/2022] [Accepted: 09/22/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.
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Affiliation(s)
- Ji-Qiao Liu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Jia-Yu Ren
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Xiao-Lan Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Li-Yan Xiong
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian 116000, Liaoning Province, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern 3003, Switzerland
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
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Vilas-Boas F, Ribeiro T, Afonso J, Cardoso H, Lopes S, Moutinho-Ribeiro P, Ferreira J, Mascarenhas-Saraiva M, Macedo G. Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study. Diagnostics (Basel) 2022; 12:diagnostics12092041. [PMID: 36140443 PMCID: PMC9498252 DOI: 10.3390/diagnostics12092041] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 12/12/2022] Open
Abstract
Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.
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Affiliation(s)
- Filipe Vilas-Boas
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Susana Lopes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Moutinho-Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Miguel Mascarenhas-Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Correspondence:
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- World Gastroenterology Organisation Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Spadaccini M, Koleth G, Emmanuel J, Khalaf K, Facciorusso A, Grizzi F, Hassan C, Colombo M, Mangiavillano B, Fugazza A, Anderloni A, Carrara S, Repici A. Enhanced endoscopic ultrasound imaging for pancreatic lesions: The road to artificial intelligence. World J Gastroenterol 2022; 28:3814-3824. [PMID: 36157539 PMCID: PMC9367228 DOI: 10.3748/wjg.v28.i29.3814] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/07/2022] [Accepted: 07/05/2022] [Indexed: 02/06/2023] Open
Abstract
Early detection of pancreatic cancer has long eluded clinicians because of its insidious nature and onset. Often metastatic or locally invasive when symptomatic, most patients are deemed inoperable. In those who are symptomatic, multi-modal imaging modalities evaluate and confirm pancreatic ductal adenocarcinoma. In asymptomatic patients, detected pancreatic lesions can be either solid or cystic. The clinical implications of identifying small asymptomatic solid pancreatic lesions (SPLs) of < 2 cm are tantamount to a better outcome. The accurate detection of SPLs undoubtedly promotes higher life expectancy when resected early, driving the development of existing imaging tools while promoting more comprehensive screening programs. An imaging tool that has matured in its reiterations and received many image-enhancing adjuncts is endoscopic ultrasound (EUS). It carries significant importance when risk stratifying cystic lesions and has substantial diagnostic value when combined with fine needle aspiration/biopsy (FNA/FNB). Adjuncts to EUS imaging include contrast-enhanced harmonic EUS and EUS-elastography, both having improved the specificity of FNA and FNB. This review intends to compile all existing enhancement modalities and explore ongoing research around the most promising of all adjuncts in the field of EUS imaging, artificial intelligence.
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Affiliation(s)
- Marco Spadaccini
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Glenn Koleth
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - James Emmanuel
- Department of Gastroenterology and Hepatology, Queen Elizabeth, Kota Kinabalu 88200, Sabah, Malaysia
| | - Kareem Khalaf
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Antonio Facciorusso
- Section of Gastroenterology, Department of Medical Sciences, University of Foggia, Foggia 71122, Italy
| | - Fabio Grizzi
- Department of Immunology and Inflammation, Humanitas Clinical and Research Hospital, Rozzano 20089, Italy
| | - Cesare Hassan
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Matteo Colombo
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Benedetto Mangiavillano
- Digestive Endoscopy Unit, Division of Gasteroenterology, Humanitas Mater Domini, Castellanza 21053, Italy
| | - Alessandro Fugazza
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Andrea Anderloni
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Silvia Carrara
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Research Hospital and University, Milan 20800, Italy
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Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022; 12:973999. [PMID: 35982967 PMCID: PMC9380440 DOI: 10.3389/fonc.2022.973999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Purpose We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021. Methods Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis. Results A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field. Conclusion The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.
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Affiliation(s)
- Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Feixiong Zhang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiaoli Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiangkun Meng
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Yu Miao
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | | | - Li Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
| | - Zhaoshen Li
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
- Clinical Medical College, Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
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50
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Ruano J, Jaramillo M, Gómez M, Romero E. Robust Descriptor of Pancreatic Tissue for Automatic Detection of Pancreatic Cancer in Endoscopic Ultrasonography. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1602-1614. [PMID: 35613973 DOI: 10.1016/j.ultrasmedbio.2022.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/06/2022] [Accepted: 04/10/2022] [Indexed: 06/15/2023]
Abstract
Pancreatic cancer (PC) has a reported mortality of 98% and a 5-y survival rate of 6.7%. Experienced gastroenterologists detect 80% of those with early-stage PC by endoscopic ultrasonography (EUS). Here we propose an automatic second reader strategy to detect PC in an entire EUS procedure, rather than focusing on pre-selected frames, as the state-of-the-art methods do. The method unmasks echo tumoral patterns in frames with a high probability of tumor. First, speeded up robust features define a set of interest points with correlated heterogeneities among different filtering scales. Afterward, intensity gradients of each interest point are summarized by 64 features at certain locations and scales. A frame feature vector is built by concatenating statistics of each feature of the 15 groups of scales. Then, binary classification is performed by Support Vector Machine and Adaboost models. Evaluation was performed using a data set comprising 55 participants, 18 of PC class (16,585 frames) and 37 subjects of non-PC class (49,664 frames), randomly splitting 10 times. The proposed method reached an accuracy of 92.1%, sensitivity of 96.3% and specificity of 87.8.3%. The observed results are also stable in noisy experiments while deep learning approaches fail to maintain similar performance.
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Affiliation(s)
- Josué Ruano
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - María Jaramillo
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Martín Gómez
- Medicina Interna, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia.
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