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Shin Y, Lee M, Lee Y, Kim K, Kim T. Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions. Life (Basel) 2025; 15:654. [PMID: 40283208 PMCID: PMC12028931 DOI: 10.3390/life15040654] [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: 03/11/2025] [Revised: 04/09/2025] [Accepted: 04/14/2025] [Indexed: 04/29/2025] Open
Abstract
Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration-particularly convolutional and recurrent neural networks-across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence's potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.
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Affiliation(s)
- Yoojin Shin
- College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.S.); (M.L.); (Y.L.)
| | - Mingyu Lee
- College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.S.); (M.L.); (Y.L.)
| | - Yoonji Lee
- College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.S.); (M.L.); (Y.L.)
| | - Kyuri Kim
- College of Medicine, Ewha Womans University, 25 Magokdong-ro 2-gil, Gangseo-gu, Seoul 07804, Republic of Korea;
| | - Taejung Kim
- Department of Hospital Pathology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 10, 63-ro, Yeongdeungpo-gu, Seoul 07345, Republic of Korea
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Alhithlool AW, Almutlaq AS, Almulla SA, Alhamdan AB, Alotaibi ZB, AlHithlool AW, Kamal AH, Daoud MYI, Zakaria OM. How do medical students perceive the role of artificial intelligence in management of gastroesophageal reflux disease? MEDICAL TEACHER 2024:1-7. [PMID: 39436823 DOI: 10.1080/0142159x.2024.2407129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 09/17/2024] [Indexed: 10/25/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has significantly revolutionized the diagnosis and treatment of various medical and surgical conditions, including gastroesophageal reflux disease (GORD). AI has the potential to enhance diagnostic and treatment capabilities, contributing to overall advancements in healthcare. The current study aimed to investigate the medical students' views regarding the use of AI in GORD management. METHODS An anonymous, self-administered questionnaire was distributed among undergraduate medical students of various grades within different national medical institutions. The questionnaire comprised three sections, addressing sociodemographic data, knowledge, and attitudes. Knowledge and attitudes were assessed through 5- and 7-item questionnaires, respectively. The knowledge scores constituted a scale of 0-5. This reflected varying levels of understanding. Categories include poor knowledge (two or less), moderate knowledge (three), and good knowledge (4 and 5). Attitudes were classified as negative, neutral, or positive based on 50% and 75% cutoff points, with scores below 50% indicating negative attitudes, 50-75% considered neutral, and scores above 75% reflecting positive attitudes. RESULTS A total of 506 medical students participated, including 273 females and 233 males, with a ratio of 1.2-1. The majority fell within the age range of 20-26 years. Additionally, 388 participants (76.7%) reported grade point averages (GPAs) higher than 4. Regarding knowledge, 9% demonstrated a poor score of knowledge, while 33% had a moderate knowledge score. However, 65% of the participating students held a neutral attitude toward the role of AI in GORD management, with 6.9% expressing a negative stance on the matter. CONCLUSION Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology. This may highlight the necessity for active involvement in AI education within the medical curricula.
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Affiliation(s)
| | | | - Sarah A Almulla
- College of Medicine, King Faisal University, Alhasa, Saudi Arabia
| | | | - Ziyad B Alotaibi
- College of Medicine, King Faisal University, Alhasa, Saudi Arabia
| | - Amjad W AlHithlool
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ahmed Hassan Kamal
- Department of Surgery, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Mohamed Yasser I Daoud
- Department of Surgery, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Ossama M Zakaria
- Department of Surgery, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia
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Janaki R, Lakshmi D. Hybrid model-based early diagnosis of esophageal disorders using convolutional neural network and refined logistic regression. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2024; 2024:19. [DOI: 10.1186/s13640-024-00634-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 07/28/2024] [Indexed: 01/04/2025]
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
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Affiliation(s)
- Deyu Zhang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Chang Wu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhenghui Yang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Yue Liu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Wanshun Li
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhendong Jin
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
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Abraham A, Jose R, Ahmad J, Joshi J, Jacob T, Khalid AUR, Ali H, Patel P, Singh J, Toma M. Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps. J Imaging 2023; 9:215. [PMID: 37888322 PMCID: PMC10607441 DOI: 10.3390/jimaging9100215] [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: 08/07/2023] [Revised: 09/29/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.
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Affiliation(s)
- Adriel Abraham
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Rejath Jose
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Jawad Ahmad
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Jai Joshi
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Thomas Jacob
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Aziz-ur-rahman Khalid
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
| | - Hassam Ali
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Internal Medicine, Brody School of Medicine, East Carolina University, Greenville, NC 27858, USA;
| | - Pratik Patel
- Department of Gastroenterology, Northwell Mather Hospital, Port Jefferson, NY 11777, USA (J.S.)
| | - Jaspreet Singh
- Department of Gastroenterology, Northwell Mather Hospital, Port Jefferson, NY 11777, USA (J.S.)
| | - Milan Toma
- New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY 11568, USA; (A.A.); (R.J.); (J.A.); (J.J.); (T.J.); (A.-u.-r.K.)
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Mohan A, Asghar Z, Abid R, Subedi R, Kumari K, Kumar S, Majumder K, Bhurgri AI, Tejwaney U, Kumar S. Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review. Ann Med Surg (Lond) 2023; 85:4920-4927. [PMID: 37811030 PMCID: PMC10553069 DOI: 10.1097/ms9.0000000000001175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/05/2023] [Indexed: 10/10/2023] Open
Abstract
Esophageal cancer is a major cause of cancer-related mortality worldwide, with significant regional disparities. Early detection of precursor lesions is essential to improve patient outcomes. Artificial intelligence (AI) techniques, including deep learning and machine learning, have proved to be of assistance to both gastroenterologists and pathologists in the diagnosis and characterization of upper gastrointestinal malignancies by correlating with the histopathology. The primary diagnostic method in gastroenterology is white light endoscopic evaluation, but conventional endoscopy is partially inefficient in detecting esophageal cancer. However, other endoscopic modalities, such as narrow-band imaging, endocytoscopy, and endomicroscopy, have shown improved visualization of mucosal structures and vasculature, which provides a set of baseline data to develop efficient AI-assisted predictive models for quick interpretation. The main challenges in managing esophageal cancer are identifying high-risk patients and the disease's poor prognosis. Thus, AI techniques can play a vital role in improving the early detection and diagnosis of precursor lesions, assisting gastroenterologists in performing targeted biopsies and real-time decisions of endoscopic mucosal resection or endoscopic submucosal dissection. Combining AI techniques and endoscopic modalities can enhance the diagnosis and management of esophageal cancer, improving patient outcomes and reducing cancer-related mortality rates. The aim of this review is to grasp a better understanding of the application of AI in the diagnosis, treatment, and prognosis of esophageal cancer and how computer-aided diagnosis and computer-aided detection can act as vital tools for clinicians in the long run.
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Affiliation(s)
| | | | - Rabia Abid
- Liaquat College of Medicine and Dentistry
| | - Rasish Subedi
- Universal College of Medical Sciences, Siddharthanagar, Nepal
| | | | | | | | - Aqsa I. Bhurgri
- Shaheed Muhtarma Benazir Bhutto Medical University, Larkana, Pakistan
| | | | - Sarwan Kumar
- Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh
- Wayne State University, Michigan, USA
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Popovic D, Glisic T, Milosavljevic T, Panic N, Marjanovic-Haljilji M, Mijac D, Stojkovic Lalosevic M, Nestorov J, Dragasevic S, Savic P, Filipovic B. The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy. Diagnostics (Basel) 2023; 13:2862. [PMID: 37761229 PMCID: PMC10528171 DOI: 10.3390/diagnostics13182862] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.
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Affiliation(s)
- Dusan Popovic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Tijana Glisic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | | | - Natasa Panic
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Marija Marjanovic-Haljilji
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Dragana Mijac
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Milica Stojkovic Lalosevic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Jelena Nestorov
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Sanja Dragasevic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Predrag Savic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Surgery, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia
| | - Branka Filipovic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
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Wang L, Yang Y, Yang A, Li T. Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images. BIOMEDICAL OPTICS EXPRESS 2023; 14:4677-4695. [PMID: 37791283 PMCID: PMC10545198 DOI: 10.1364/boe.487456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/11/2023] [Accepted: 06/29/2023] [Indexed: 10/05/2023]
Abstract
Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.
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Affiliation(s)
- Lingxiao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Yingyun Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Aiming Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
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Liao WC, Mukundan A, Sadiaza C, Tsao YM, Huang CW, Wang HC. Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:4383-4405. [PMID: 37799695 PMCID: PMC10549751 DOI: 10.1364/boe.492635] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 10/07/2023]
Abstract
One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.
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Affiliation(s)
- Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Cleorita Sadiaza
- Department of Mechanical Engineering, Far Eastern University, P. Paredes St., Sampaloc, Manila, 1015, Philippines
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi, 62247, Taiwan
- Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan
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Kim HY. Clinical features of gastric adenoma detected within 3 years after negative screening endoscopy in Korea. Gastroenterol Rep (Oxf) 2023; 11:goad039. [PMID: 37408567 PMCID: PMC10318390 DOI: 10.1093/gastro/goad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/20/2023] [Accepted: 06/09/2023] [Indexed: 07/07/2023] Open
Abstract
Background Early detection and management of gastric adenoma are important for preventing gastric cancer. The present study aimed to evaluate the predictors of missed gastric adenoma on screening endoscopy in Korea and identify the risk factors associated with interval precancerous gastric lesions. Methods All cases of gastric adenomas diagnosed via screening endoscopy between 2007 and 2019 were reviewed. Among them, those who had undergone endoscopy within 3 years were included in the present study. Missed gastric adenoma was defined as gastric adenoma diagnosed within 3 years after negative screening endoscopy. Results In total, 295 cases of gastric adenoma were identified. Of these, 95 (32.2%) were missed gastric adenoma cases (mean age, 60.6 years; average interval between final and index endoscopies, 12.6 months); the remaining 200 (67.8%) were newly detected adenoma cases. Univariate analysis revealed that male sex, endoscopist experience, observation time, and presence of gastric intestinal metaplasia (pathologically proven) were associated with missed gastric adenoma. Multivariate analysis revealed that gastric intestinal metaplasia (odds ratio [OR], 2.736; 95% confidence interval [CI], 1.320-5.667; P = 0.007) and shorter observation time of the index screening endoscopy (B, -0.011; OR, 0.990; 95% CI, 0.986-0.993; P < 0.001) were independent risk factors for missed gastric adenoma. The optimal cut-off for the observation time for detecting gastric adenoma was 3.53 minutes (area under curve, 0.738; 95% CI, 0.677-0.799; P < 0.001). Conclusions Gastric intestinal metaplasia is an indication of missed gastric adenoma. Therefore, careful inspection of gastric mucosa with gastric intestinal metaplasia and proper observation time can lower the possibility of missing the gastric adenoma during screening.
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Affiliation(s)
- Hyun Young Kim
- Corresponding author. Department of Internal Medicine, Health Promotion Center, Seoul National University Bundang Hospital, 82, Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620 South Korea. Tel: + 82-31-787-7008; Fax: + 82-31-787-4051;
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Li M, Chen C, Cao Y, Zhou P, Deng X, Liu P, Wang Y, Lv X, Chen C. CIABNet: Category imbalance attention block network for the classification of multi-differentiated types of esophageal cancer. Med Phys 2023; 50:1507-1527. [PMID: 36272103 DOI: 10.1002/mp.16067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/25/2022] [Accepted: 09/09/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Esophageal cancer has become one of the important cancers that seriously threaten human life and health, and its incidence and mortality rate are still among the top malignant tumors. Histopathological image analysis is the gold standard for diagnosing different differentiation types of esophageal cancer. PURPOSE The grading accuracy and interpretability of the auxiliary diagnostic model for esophageal cancer are seriously affected by small interclass differences, imbalanced data distribution, and poor model interpretability. Therefore, we focused on developing the category imbalance attention block network (CIABNet) model to try to solve the previous problems. METHODS First, the quantitative metrics and model visualization results are integrated to transfer knowledge from the source domain images to better identify the regions of interest (ROI) in the target domain of esophageal cancer. Second, in order to pay attention to the subtle interclass differences, we propose the concatenate fusion attention block, which can focus on the contextual local feature relationships and the changes of channel attention weights among different regions simultaneously. Third, we proposed a category imbalance attention module, which treats each esophageal cancer differentiation class fairly based on aggregating different intensity information at multiple scales and explores more representative regional features for each class, which effectively mitigates the negative impact of category imbalance. Finally, we use feature map visualization to focus on interpreting whether the ROIs are the same or similar between the model and pathologists, thus better improving the interpretability of the model. RESULTS The experimental results show that the CIABNet model outperforms other state-of-the-art models, which achieves the most advanced results in classifying the differentiation types of esophageal cancer with an average classification accuracy of 92.24%, an average precision of 93.52%, an average recall of 90.31%, an average F1 value of 91.73%, and an average AUC value of 97.43%. In addition, the CIABNet model has essentially similar or identical to the ROI of pathologists in identifying histopathological images of esophageal cancer. CONCLUSIONS Our experimental results prove that our proposed computer-aided diagnostic algorithm shows great potential in histopathological images of multi-differentiated types of esophageal cancer.
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Affiliation(s)
- Min Li
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
- Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
- Xinjiang Cloud Computing Application Laboratory, Karamay, China
| | - Yanzhen Cao
- Department of Pathology, The Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
| | - Panyun Zhou
- College of Software, Xinjiang University, Urumqi, China
| | - Xin Deng
- College of Software, Xinjiang University, Urumqi, China
| | - Pei Liu
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Yunling Wang
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
- Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, China
- Xinjiang Cloud Computing Application Laboratory, Karamay, China
- College of Software, Xinjiang University, Urumqi, China
- Key Laboratory of software engineering technology, Xinjiang University, Urumqi, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, China
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Pallio S, Crinò SF, Maida M, Sinagra E, Tripodi VF, Facciorusso A, Ofosu A, Conti Bellocchi MC, Shahini E, Melita G. Endoscopic Ultrasound Advanced Techniques for Diagnosis of Gastrointestinal Stromal Tumours. Cancers (Basel) 2023; 15:1285. [PMID: 36831627 PMCID: PMC9954263 DOI: 10.3390/cancers15041285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Gastrointestinal Stromal Tumors (GISTs) are subepithelial lesions (SELs) that commonly develop in the gastrointestinal tract. GISTs, unlike other SELs, can exhibit malignant behavior, so differential diagnosis is critical to the decision-making process. Endoscopic ultrasound (EUS) is considered the most accurate imaging method for diagnosing and differentiating SELs in the gastrointestinal tract by assessing the lesions precisely and evaluating their malignant risk. Due to their overlapping imaging characteristics, endosonographers may have difficulty distinguishing GISTs from other SELs using conventional EUS alone, and the collection of tissue samples from these lesions may be technically challenging. Even though it appears to be less effective in the case of smaller lesions, histology is now the gold standard for achieving a final diagnosis and avoiding unnecessary and invasive treatment for benign SELs. The use of enhanced EUS modalities and elastography has improved the diagnostic ability of EUS. Furthermore, recent advancements in artificial intelligence systems that use EUS images have allowed them to distinguish GISTs from other SELs, thereby improving their diagnostic accuracy.
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Affiliation(s)
- Socrate Pallio
- Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy
| | | | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, 93100 Caltanissetta, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, 90015 Cefalù, Italy
| | | | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Foggia, 71100 Foggia, Italy
| | - Andrew Ofosu
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH 45201, USA
| | | | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology—IRCCS “Saverio de Bellis” Castellana Grotte, 70013 Castellana Grotte, Italy
| | - Giuseppinella Melita
- Human Pathology of Adult and Child Department, University of Messina, 98100 Messina, Italy
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Goenka MK, Afzalpurkar S, Jejurikar S, Rodge GA, Tiwari A. Role of artificial intelligence-guided esophagogastroduodenoscopy in assessing the procedural completeness and quality. Indian J Gastroenterol 2023; 42:128-135. [PMID: 36715841 DOI: 10.1007/s12664-022-01294-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/12/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS The quality of esophagogastroduodenoscopy (EGD) can have great impact on the detection of esophageal and gastric lesions, including malignancies. The aim of the study is to investigate the use of artificial intelligence (AI) during EGD by the endoscopists-in-training so that a real-time feedback can be provided, ensuring compliance to a pre-decided protocol for examination. METHODS This is an observational pilot study. The videos of the EGD procedure performed between August 1, 2021, and September 30, 2021, were prospectively analyzed using AI system. The assessment of completeness of the procedure was done based on the visualizsation of pre-defined 29 locations. Endoscopists were divided into two categories - whether they are in the training period (category A) or have competed their endoscopy training (category B). RESULTS A total of 277 procedures, which included 114 category-A and 163 category-B endoscopists, respectively, were included. Most commonly covered areas by the endoscopists were greater curvature of antrum (97.47%), second part of duodenum (96.75%), other parts of antrum such as the anterior, lesser curvature and the posterior aspect (96.75%, 94.95%, and 94.22%, respectively). Commonly missed or inadequately seen areas were vocal cords (99.28%), epiglottis (93.14%) and posterior, anterior, and lateral aspect of incisura (78.70%, 73.65%, and 73.53%, respectively). The good quality procedures were done predominantly by categoryB endoscopists (88.68% vs. 11.32%, p < 0.00001). CONCLUSION AI can play an important role in assessing the quality and completeness of EGD and can be a part of training of endoscopy in future.
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Affiliation(s)
- Mahesh Kumar Goenka
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India.
| | - Shivaraj Afzalpurkar
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | | | - Gajanan Ashokrao Rodge
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | - Awanish Tiwari
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
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Magalhães B, Neto A, Cunha A. Generative Adversarial Networks for Augmenting Endoscopy Image Datasets of Stomach Precancerous Lesions: A Review. IEEE ACCESS 2023; 11:136292-136307. [DOI: 10.1109/access.2023.3338545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
Affiliation(s)
- Bruno Magalhães
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Alexandre Neto
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - António Cunha
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
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Wang Jinming 王, Li Peng 李, Liang Yan 梁, Sun Wei 孙, Song Jie 宋, Feng Yadong 冯, Zhao Lingxiao 赵. 基于轻量化注意力残差网络的食管鳞癌识别方法. LASER & OPTOELECTRONICS PROGRESS 2023; 60:1010023. [DOI: 10.3788/lop220856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Yashima K, Onoyama T, Kurumi H, Takeda Y, Yoshida A, Kawaguchi K, Yamaguchi N, Isomoto H. Current status and future perspective of linked color imaging for gastric cancer screening: a literature review. J Gastroenterol 2023; 58:1-13. [PMID: 36287268 PMCID: PMC9825522 DOI: 10.1007/s00535-022-01934-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/14/2022] [Indexed: 02/04/2023]
Abstract
Screening endoscopy has advanced to facilitate improvements in the detection and prognosis of gastric cancer. However, most early gastric cancers (EGCs) have subtle morphological or color features that are difficult to detect by white-light imaging (WLI); thus, even well-trained endoscopists can miss EGC when using this conventional endoscopic approach. This review summarizes the current and future status of linked color imaging (LCI), a new image-enhancing endoscopy (IEE) method, for gastric screening. LCI has been shown to produce bright images even at a distant view and provide excellent visibility of gastric cancer due to high color contrast relative to the surrounding tissue. LCI delineates EGC as orange-red and intestinal metaplasia as purple, regardless of a history of Helicobacter pylori (Hp) eradication, and contributes to the detection of superficial EGC. Moreover, LCI assists in the determination of Hp infection status, which is closely related to the risk of developing gastric cancer. Transnasal endoscopy (ultra-thin) using LCI is also useful for identifying gastric neoplastic lesions. Recently, several prospective studies have demonstrated that LCI has a higher detection ratio for gastric cancer than WLI. We believe that LCI should be used in routine upper gastrointestinal endoscopies.
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Affiliation(s)
- Kazuo Yashima
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan.
| | - Takumi Onoyama
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan
| | - Hiroki Kurumi
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan
| | - Yohei Takeda
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan
| | - Akira Yoshida
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan
| | - Koichiro Kawaguchi
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan
| | - Naoyuki Yamaguchi
- Department of Endoscopy, Nagasaki University Hospital, Nagasaki, Japan
| | - Hajime Isomoto
- Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1 Nishicho, Yonago, 683-8504, Japan
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Feng J, Yu SR, Zhang YP, Qu L, Wei L, Wang PF, Zhu LJ, Bao Y, Lei XG, Gao LL, Feng YH, Yu Y, Huang XJ. A system based on deep convolutional neural network improves the detection of early gastric cancer. Front Oncol 2022; 12:1021625. [PMID: 36620563 PMCID: PMC9815521 DOI: 10.3389/fonc.2022.1021625] [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: 08/17/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Background Early gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis. Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has made significant progress in the field of gastroenterology. The purpose of this study was to establish a DCNN assist system to improve the detection of EGC. Methods 3400 EGC and 8600 benign images were collected to train the DCNN to detect EGC. Subsequently, its diagnostic ability was compared to that of endoscopists using an independent internal test set (ITS, including 1289 images) and an external test set (ETS, including 542 images) come from three digestive center. Results The diagnostic time of DCNN and endoscopists were 0.028s, 8.05 ± 0.21s, 7.69 ± 0.25s in ITS, and 0.028s, 7.98 ± 0.19s, 7.50 ± 0.23s in ETS, respectively. In ITS, the diagnostic sensitivity and accuracy of DCNN are 88.08%(95% confidence interval,95%CI,85.24%-90.44%), 88.60% (95%CI,86.74%-90.22%), respectively. In ETS, the diagnostic sensitivity and accuracy are 92.08% (95%CI, 87.91%- 94.94%),92.07%(95%CI, 89.46%-94.08%),respectively. DCNN outperformed all endoscopists in ETS, and had a significantly higher sensitivity than the junior endoscopists(JE)(by18.54% (95%CI, 15.64%-21.84%) in ITS, also higher than JE (by21.67%,95%CI, 16.90%-27.32%) and senior endoscopists (SE) (by2.08%, 95%CI, 0.75%-4.92%)in ETS. The accuracy of DCNN model was higher (by10.47%,95%CI, 8.91%-12.27%) than that of JE in ITS, and also higher (by14.58%,95%CI, 11.84%-17.81%; by 1.94%,95%CI,1.25%-2.96%, respectively) than JE and SE in ETS. Conclusion The DCNN can detected more EGC images in a shorter time than the endoscopists. It will become an effective tool to assist in the detection of EGC in the near future.
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Affiliation(s)
- Jie Feng
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China,Technology Research and Development Department, Digestive Endoscopy Engineering Research and Development Center of Gansu Province, Lanzhou, Gansu, China
| | - Shang rui Yu
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Yao ping Zhang
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China,Technology Research and Development Department, Digestive Endoscopy Engineering Research and Development Center of Gansu Province, Lanzhou, Gansu, China
| | - Lina Qu
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China,Technology Research and Development Department, Digestive Endoscopy Engineering Research and Development Center of Gansu Province, Lanzhou, Gansu, China
| | - Lina Wei
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Peng fei Wang
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Li juan Zhu
- Department of Sciences and Technology, Beijing Huag gen Anbang Technology Technology Company Limited, Beijing, China
| | - Yanfeng Bao
- Department of Sciences and Technology, Beijing Huag gen Anbang Technology Technology Company Limited, Beijing, China
| | - Xiao gang Lei
- Department of Gastroenterology, Lanzhou Cheng guan District People’s Hospital, Lanzhou, Gansu, China
| | - Liang liang Gao
- Department of Gastroenterology, Min County People’s Hospital, Ding Xi, Gansu, China
| | - Yan hu Feng
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Yi Yu
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China
| | - Xiao jun Huang
- Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu, China,Technology Research and Development Department, Digestive Endoscopy Engineering Research and Development Center of Gansu Province, Lanzhou, Gansu, China,*Correspondence: Xiao jun Huang,
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Shen Q, Chen H. A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach. Front Oncol 2022; 12:887841. [PMID: 36568200 PMCID: PMC9768177 DOI: 10.3389/fonc.2022.887841] [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: 03/04/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Objective To develop and validate a deep learning predictive model with better performance in survival estimation of esophageal adenocarcinoma (EAC). Method Cases diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A deep learning survival neural network was developed and validated based on 17 variables, including demographic information, clinicopathological characteristics, and treatment details. Based on the total risk score derived from this algorithm, a novel risk classification system was constructed and compared with the 8th edition of the tumor, node, and metastasis (TNM) staging system. Results Of 7,764 EAC patients eligible for the study, 6,818 (87.8%) were men and the median (interquartile range, IQR) age was 65 (58-72) years. The deep learning model generated significantly superior predictions to the 8th edition staging system on the test data set (C-index: 0.773 [95% CI, 0.757-0.789] vs. 0.683 [95% CI, 0.667-0.699]; P < 0.001). Calibration curves revealed that the deep learning model was well calibrated for 1- and 3-year OS, most points almost directly distributing on the 45° line. Decision curve analyses (DCAs) showed that the novel risk classification system exhibited a more significant positive net benefit than the TNM staging system. A user-friendly and precise web-based calculator with a portably executable file was implemented to visualize the deep learning predictive model. Conclusion A deep learning predictive model was developed and validated, which possesses more excellent calibration and discrimination abilities in survival prediction of EAC. The novel risk classification system based on the deep learning algorithm may serve as a useful tool in clinical decision making given its easy-to-use and better clinical applicability.
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Affiliation(s)
- Qiang Shen
- Department of General Surgery, Ningbo No.9 Hospital, Ningbo, Zhejiang, China
| | - Hongyu Chen
- Department of Thoracic Surgery, Ningbo No.9 Hospital, Ningbo, Zhejiang, China,*Correspondence: Hongyu Chen, chenhongyu0119@163
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20
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An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning. Diagnostics (Basel) 2022; 12:diagnostics12112827. [PMID: 36428887 PMCID: PMC9689126 DOI: 10.3390/diagnostics12112827] [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: 09/30/2022] [Revised: 11/05/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022] Open
Abstract
Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured.
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Okumura S, Goudo M, Hiwa S, Yasuda T, Kitae H, Yasuda Y, Tomie A, Omatsu T, Ichikawa H, Yagi N, Hiroyasu T. Demarcation Line Determination for Diagnosis of Gastric Cancer Disease Range Using Unsupervised Machine Learning in Magnifying Narrow-Band Imaging. Diagnostics (Basel) 2022; 12:diagnostics12102491. [PMID: 36292179 PMCID: PMC9600716 DOI: 10.3390/diagnostics12102491] [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: 09/12/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 11/25/2022] Open
Abstract
Background and Aims: It is important to determine an accurate demarcation line (DL) between the cancerous lesions and background mucosa in magnifying narrow-band imaging (M-NBI)-based diagnosis. However, it is difficult for novice endoscopists. We aimed to automatically determine the accurate DL using a machine learning method. Methods: We used an unsupervised machine learning approach to determine the DLs. Our method consists of the following four steps: (1) an M-NBI image is segmented into superpixels using simple linear iterative clustering; (2) the image features are extracted for each superpixel; (3) the superpixels are grouped into several clusters using the k-means method; and (4) the boundaries of the clusters are extracted as DL candidates. The 23 M-NBI images of 11 cases were used for performance evaluation. The evaluation investigated the similarity of the DLs identified by endoscopists and our method, and the Euclidean distance between the two DLs was calculated. For the single case of 11 cases, the histopathological examination was also conducted to evaluate the proposed system. Results: The average Euclidean distances for the 11 cases were 10.65, 11.97, 7.82, 8.46, 8.59, 9.72, 12.20, 9.06, 22.86, 8.45, and 25.36. The results indicated that the proposed method could identify similar DLs to those identified by experienced doctors. Additionally, it was confirmed that the proposed system could generate pathologically valid DLs by increasing the number of clusters. Conclusions: Our proposed system can support the training of inexperienced doctors as well as enrich the knowledge of experienced doctors in endoscopy.
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Affiliation(s)
- Shunsuke Okumura
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto 610-0394, Japan
| | - Misa Goudo
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto 610-0394, Japan
| | - Satoru Hiwa
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto 610-0394, Japan
- Correspondence:
| | - Takeshi Yasuda
- Department of Gastroenterology, Asahi University Hospital, Gifu 500-8523, Japan
| | - Hiroaki Kitae
- Department of Gastroenterology, Asahi University Hospital, Gifu 500-8523, Japan
| | - Yuriko Yasuda
- Department of Gastroenterology, Asahi University Hospital, Gifu 500-8523, Japan
| | - Akira Tomie
- Department of Gastroenterology, Asahi University Hospital, Gifu 500-8523, Japan
| | - Tatsushi Omatsu
- Department of Gastroenterology, Asahi University Hospital, Gifu 500-8523, Japan
| | - Hiroshi Ichikawa
- Department of Medical Life Systems, Doshisha University, Kyoto 610-0394, Japan
| | - Nobuaki Yagi
- Department of Gastroenterology, Asahi University Hospital, Gifu 500-8523, Japan
| | - Tomoyuki Hiroyasu
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto 610-0394, Japan
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Li Z, Wu R, Gan T. Study on image data cleaning method of early esophageal cancer based on VGG_NIN neural network. Sci Rep 2022; 12:14323. [PMID: 35995817 PMCID: PMC9395400 DOI: 10.1038/s41598-022-18707-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 08/17/2022] [Indexed: 12/24/2022] Open
Abstract
In order to clean the mislabeled images in the esophageal endoscopy image data set, we designed a new neural network VGG_NIN. Based on the new neural network structure, we developed a method to clean the mislabeled images in the esophageal endoscopy image data set. To verify the effectiveness of the proposed method, we designed two experiments using 3835 esophageal endoscopy images provided by West China Hospital of Sichuan University. The experimental results showed that the proposed method could clean about 93% of the mislabeled images in the data set, which was the first time in the cleaning of esophageal endoscopy image data set. Finally, in order to verify the generalization ability of this method, we cleaned the Kaggle open cat and dog data set, and cleaned out about 167 mislabeled images. Therefore, the proposed method can effectively screen the mislabeled images in the esophageal endoscopy image data set and has good generalization ability, which can provide great help for the development of high-performance gastrointestinal endoscopy image analysis model.
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Affiliation(s)
- Zhengwen Li
- College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
| | - Runmin Wu
- School of Education, Sichuan Open University of China, Chengdu, 610031, China
| | - Tao Gan
- West China Hospital, Sichuan University, Chengdu, 611731, China
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Zhao Q, Jia Q, Chi T. Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study. BMC Gastroenterol 2022; 22:352. [PMID: 35879649 PMCID: PMC9310473 DOI: 10.1186/s12876-022-02427-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/15/2022] [Indexed: 11/28/2022] Open
Abstract
Background and aims Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is only 42%. Therefore, we developed a real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net deep learning (DL) and conducted a prospective nested case–control study to evaluate the diagnostic evaluation indices of the model and its consistency with pathological diagnosis.
Methods Our cohort consisted of 1539 patients undergoing gastroscopy from December 1, 2020, to July 1, 2021. Based on pathological diagnosis, patients in the cohort were divided into the CAG group or the chronic nonatrophic gastritis (CNAG) group, and we assessed the diagnostic evaluation indices of this model and its consistency with pathological diagnosis after propensity score matching (PSM) to minimize selection bias in the study. Results After matching, the diagnostic evaluation indices and consistency evaluation of the model were better than those of endoscopists [sensitivity (84.02% vs. 62.72%), specificity (97.04% vs. 81.95%), positive predictive value (96.60% vs. 77.66%), negative predictive value (85.86% vs. 68.73%), accuracy rate (90.53% vs. 72.34%), Youden index (81.06% vs. 44.67%), odd product (172.5 vs. 7.64), positive likelihood ratio (28.39 vs. 3.47), negative likelihood ratio (0.16 vs. 0.45), AUC (95% CI) [0.909 (0.884–0.934) vs. 0.740 (0.702–0.778)] and Kappa (0.852 vs. 0.558)]. Conclusions Our prospective nested case–control study proved that the diagnostic evaluation indices and consistency evaluation of the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL were superior to those of endoscopists. Trial registrationChiCTR2100044458, 18/03/2020.
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Affiliation(s)
- Quchuan Zhao
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China
| | - Qing Jia
- Department of Anesthesiology, Guang'anmen Hospital China Academy of Chinese Medical Sciences, 5 North Court Street, Beijing, 100053, China.
| | - Tianyu Chi
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China.
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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
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Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Fu XY, Mao XL, Chen YH, You NN, Song YQ, Zhang LH, Cai Y, Ye XN, Ye LP, Li SW. The Feasibility of Applying Artificial Intelligence to Gastrointestinal Endoscopy to Improve the Detection Rate of Early Gastric Cancer Screening. Front Med (Lausanne) 2022; 9:886853. [PMID: 35652070 PMCID: PMC9150174 DOI: 10.3389/fmed.2022.886853] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/06/2022] [Indexed: 12/24/2022] Open
Abstract
Convolutional neural networks in the field of artificial intelligence show great potential in image recognition. It assisted endoscopy to improve the detection rate of early gastric cancer. The 5-year survival rate for advanced gastric cancer is less than 30%, while the 5-year survival rate for early gastric cancer is more than 90%. Therefore, earlier screening for gastric cancer can lead to a better prognosis. However, the detection rate of early gastric cancer in China has been extremely low due to many factors, such as the presence of gastric cancer without obvious symptoms, difficulty identifying lesions by the naked eye, and a lack of experience among endoscopists. The introduction of artificial intelligence can help mitigate these shortcomings and greatly improve the accuracy of screening. According to relevant reports, the sensitivity and accuracy of artificial intelligence trained on deep cirrocumulus neural networks are better than those of endoscopists, and evaluations also take less time, which can greatly reduce the burden on endoscopists. In addition, artificial intelligence can also perform real-time detection and feedback on the inspection process of the endoscopist to standardize the operation of the endoscopist. AI has also shown great potential in training novice endoscopists. With the maturity of AI technology, AI has the ability to improve the detection rate of early gastric cancer in China and reduce the death rate of gastric cancer related diseases in China.
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Affiliation(s)
- Xin-yu Fu
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Xin-li Mao
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
- Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ya-hong Chen
- Health Management Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ning-ning You
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ya-qi Song
- Taizhou Hospital, Zhejiang University, Linhai, China
| | - Li-hui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yue Cai
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Xing-nan Ye
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Linhai, China
| | - Li-ping Ye
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
- Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Shao-wei Li
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
- Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
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Penrice DD, Rattan P, Simonetto DA. Artificial Intelligence and the Future of Gastroenterology and Hepatology. GASTRO HEP ADVANCES 2022; 1:581-595. [PMID: 39132066 PMCID: PMC11307848 DOI: 10.1016/j.gastha.2022.02.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/22/2022] [Indexed: 08/13/2024]
Abstract
The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.
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Affiliation(s)
- Daniel D. Penrice
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Puru Rattan
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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Kutsumi H. Contribution of the Japan Gastroenterological Endoscopy Society to promote computer-aided diagnosis/detection system development using artificial intelligence technology. Dig Endosc 2022; 34 Suppl 2:132-135. [PMID: 34652003 DOI: 10.1111/den.14146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Hiromu Kutsumi
- Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan
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28
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Ye XH, Zhao LL, Wang L. Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta-analysis. J Dig Dis 2022; 23:253-261. [PMID: 35793389 DOI: 10.1111/1751-2980.13110] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/21/2022] [Accepted: 07/01/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Gastrointestinal stromal tumors (GISTs) are thought to have a malignant potential. However, utilizing endoscopic ultrasound (EUS) to differentiate GISTs from other types of subepithelial lesions (SELs) remains challenging. Artificial intelligence (AI)-based diagnostic systems for EUS have been reported to have a promising performance, although the results of the previous studies remain controversial. In this meta-analysis, we aimed to assess the diagnostic accuracy of AI-based EUS in distinguishing GISTs from other SELs. METHODS A literature search was conducted on MEDLINE and EMBASE databases to identify relevant articles. The sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUROC) of eligible studies were analyzed. RESULTS Seven studies were eligible for the final analysis. The combined sensitivity and specificity of AI-based EUS were 0.93 (95% confidence interval [CI] 0.88-0.96) and 0.78 (95% CI 0.67-0.87), respectively. The overall diagnostic odds ratio of AI-based EUS for GISTs was 36.74 (95% CI 17.69-76.30) with an AUROC of 0.94. CONCLUSIONS AI-based EUS showed high diagnostic ability and might help better differentiate GISTs from other SELs. More prospective studies on the diagnosis of GISTs using AI-based EUS are warranted in clinical setting.
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Affiliation(s)
- Xiao Hua Ye
- Department of Gastroenterology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang Province, China
| | - Lin Lin Zhao
- Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Lei Wang
- Department of Gastroenterology, Digestive Endoscopy Center, Changhai Hospital, Naval Medical University, Shanghai, China
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Abe S, Makiguchi ME, Nonaka S, Suzuki H, Yoshinaga S, Saito Y. Emerging texture and color enhancement imaging in early gastric cancer. Dig Endosc 2022; 34:714-720. [PMID: 34716942 DOI: 10.1111/den.14182] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/28/2021] [Indexed: 12/14/2022]
Abstract
Screening endoscopy improves detection and prognosis of patients with gastric cancer. However, even expert endoscopists can miss early gastric cancer under standard white light imaging. Texture and color enhancement imaging (TXI) is an image-enhanced endoscopy that enhances brightness, surface irregularities such elevation or depression, and subtle color changes. A few image-oriented studies have compared the gastric color differences between neoplastic and peripheral areas under both white light imaging and TXI. The results not only suggested that the overall color differences to be more pronounced in TXI, but also that TXI mode 1 was superior to white light imaging in the visibility of early gastric cancer. Despite the promising results in these initial studies, it is unclear whether the superiority of the image-enhanced endoscopy will translate into an improvement in early gastric cancer detection in real practice. Therefore, large-scale prospective studies are necessary to investigate the efficacy of this new technology in the evaluation of patients undergoing screening endoscopy.
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Affiliation(s)
- Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Satoru Nonaka
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Haruhisa Suzuki
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | | | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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30
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Zhao Q, Chi T. Deep learning model can improve the diagnosis rate of endoscopic chronic atrophic gastritis: a prospective cohort study. BMC Gastroenterol 2022; 22:133. [PMID: 35321641 PMCID: PMC8941797 DOI: 10.1186/s12876-022-02212-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 03/15/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND AIMS Chronic atrophic gastritis (CAG) is a precancerous form of gastric cancer. However, with pathological diagnosis as the gold standard, the sensitivity of endoscopic diagnosis of atrophy is only 42%. We developed a deep learning (DL)-based real-time video monitoring diagnostic model for endoscopic CAG and conducted a prospective cohort study to verify whether this diagnostic model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists. METHODS A U-NET network was used to build a real-time video monitoring diagnostic model for endoscopic CAG based on DL. We enrolled 431 patients who underwent gastroscopy from October 1, 2020, to December 1, 2020. To keep the baseline data of enrolled patient uniform and control for confounding factors, we applied a paired design and included the same patients in both the DL and the endoscopist group. RESULTS The DL model improved the diagnosis rate of endoscopic CAG compared with that of endoscopists. Compared with diagnoses by endoscopists, the proportions of moderate and severe CAG in the atrophy patients diagnosed by the DL model were significantly larger, the proportion of "type O" CAG was significantly larger, the number of atrophy sites found was significantly increased, and the number of biopsies was significantly decreased. Compared with diagnoses by endoscopists, in the atrophic lesions diagnosed by the DL model, the proportions of severe atrophy and severe intestinal metaplasia were significantly increased. CONCLUSIONS Our study suggested the DL model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists. TRIAL REGISTRATION ChiCTR2100044458, 18/03/2020.
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Affiliation(s)
- Quchuan Zhao
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China
| | - Tianyu Chi
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China.
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31
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Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022; 55:528-540. [PMID: 35098562 PMCID: PMC9305819 DOI: 10.1111/apt.16778] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/09/2022] [Accepted: 01/09/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). AIM We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. METHODS We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. RESULTS For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. CONCLUSIONS AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
| | - Brigida Barberio
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
- Department of Medical ScienceUniversity of FerraraFerraraItaly
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas UniversityVia Rita Levi Montalcini 420072 Pieve Emanuele, MilanItaly
- IRCCS Humanitas Research Hospitalvia Manzoni 5620089 Rozzano, MilanItaly
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical CenterKansas CityMissouriUSA
| | - Edoardo Savarino
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Nicola de Bortoli
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
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Ratcliffe E, Britton J, Hamdy S, McLaughlin J, Ang Y. Developing patient-orientated Barrett's oesophagus services: the role of dedicated services. BMJ Open Gastroenterol 2022; 9:bmjgast-2021-000829. [PMID: 35193888 PMCID: PMC8867250 DOI: 10.1136/bmjgast-2021-000829] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/09/2022] [Indexed: 11/03/2022] Open
Abstract
Introduction Barrett’s oesophagus (BO) is common and is a precursor to oesophageal adenocarcinoma with a 0.33% per annum risk of progression. Surveillance and follow-up services for BO have been shown to be lacking, with studies showing inadequate adherence to guidelines and patients reporting a need for greater disease-specific knowledge. This review explores the emerging role of dedicated services for patients with BO. Methods A literature search of PubMed, MEDLINE, Embase, Emcare, HMIC, BNI, CiNAHL, AMED and PsycINFO in regard to dedicated BO care pathways was undertaken. Results Prospective multicentre and randomised trials were lacking. Published cohort data are encouraging with improvements in guideline adherence with dedicated services, with one published study showing significant improvements in dysplasia detection rates. Accuracy of allocation to surveillance endoscopy has been shown to hold cost savings, and a study of a dedicated clinic showed increased discharges from unnecessary surveillance. Training modalities for BO surveillance and dysplasia detection exist, which could be used to educate a BO workforce. Qualitative and quantitative studies have shown patients report high levels of cancer worry and poor disease-specific knowledge, but few studies have explored follow-up care models despite being a patient and clinician priority for research. Conclusions Cost–benefit analysis for dedicated services, considering both financial and environmental impacts, and more robust clinical data must be obtained to support this model of care in the wider health service. Greater understanding is needed of the root causes for poor guideline adherence, and disease-specific models of care should be designed around clinical and patient-reported outcomes to address the unmet needs of patients with BO.
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Affiliation(s)
- Elizabeth Ratcliffe
- Gastroenterology, Wrightington Wigan and Leigh NHS Foundation Trust, Leigh, UK .,School of Medical Sciences, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK
| | - James Britton
- Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Shaheen Hamdy
- School of Medical Sciences, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK.,Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - John McLaughlin
- School of Medical Sciences, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK.,Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Yeng Ang
- School of Medical Sciences, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK.,Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK
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33
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Wu L, Xu M, Jiang X, He X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Guo M, Huang M, Ye L, Shen L, Yu H. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc 2022; 95:269-280.e6. [PMID: 34547254 DOI: 10.1016/j.gie.2021.09.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation, and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to August 2, 2021 in RHWU to assess clinical practice applicability. RESULTS Over 10,000 patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions and 92.9% and 91.7% for diagnosing neoplasms in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasms from that of experts (100% vs 85.5% ± 3.4% [P = .003] and 100% vs 86.4% ± 2.8% [P = .002], respectively). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04 ± 3.04 false positives per gastroscopy and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasms. CONCLUSIONS Our results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work. (Clinical trial registration number: ChiCTR2100045963.).
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Heng Zhang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaowei Ai
- Department of Gastroenterology, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, China
| | - Qiaoyun Tong
- Department of Gastroenterology, Yichang Central People's Hospital & Institute of Digestive Diseases, China Three Gorges University, Yichang, China
| | - Peihua Lv
- Spleen and Stomach Department, Jingmen Petrochemical Hospital, Jingmen, China
| | - Bin Lu
- Department of Gastroenterology, Xiaogan Central Hospital, Xiaogan, China
| | - Mingwen Guo
- Department of Gastroenterology, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, China
| | - Manling Huang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liping Ye
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, China
| | - Lei Shen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
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Ijaz A, Nabeel M, Masood U, Mahmood T, Hashmi MS, Posokhova I, Rizwan A, Imran A. Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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Wu L, Wang J, He X, Zhu Y, Jiang X, Chen Y, Wang Y, Huang L, Shang R, Dong Z, Chen B, Tao X, Wu Q, Yu H. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest Endosc 2022; 95:92-104.e3. [PMID: 34245752 DOI: 10.1016/j.gie.2021.06.033] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/30/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND AND AIMS We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition. METHODS This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients' videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs. RESULTS One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively). CONCLUSIONS The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning-based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Jing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Yiyun Chen
- School of Resources and Environmental Sciences of Wuhan University, Wuhan, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Boru Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Qi Wu
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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Kandel P, Wallace MB. Advanced Imaging Techniques and In vivo Histology: Current Status and Future Perspectives (Lower G.I.). GASTROINTESTINAL AND PANCREATICO-BILIARY DISEASES: ADVANCED DIAGNOSTIC AND THERAPEUTIC ENDOSCOPY 2022:291-310. [DOI: 10.1007/978-3-030-56993-8_110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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38
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AIM in Endoscopy Procedures. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Karstensen J, Nayahangan L, Konge L, Vilmann P. A core curriculum for basic EUS skills: An international consensus using the Delphi methodology. Endosc Ultrasound 2022; 11:122-132. [PMID: 35488624 PMCID: PMC9059795 DOI: 10.4103/eus-d-21-00125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Background and Objectives: Materials and Methods: Results: Conclusions:
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Utilization of Ultrasonic Image Characteristics Combined with Endoscopic Detection on the Basis of Artificial Intelligence Algorithm in Diagnosis of Early Upper Gastrointestinal Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2773022. [PMID: 34880973 PMCID: PMC8648460 DOI: 10.1155/2021/2773022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/31/2021] [Accepted: 11/03/2021] [Indexed: 12/26/2022]
Abstract
The aim of this study was to evaluate the diagnostic value of artificial intelligence algorithm combined with ultrasound endoscopy in early esophageal cancer and precancerous lesions by comparing the examination of conventional endoscopy and artificial intelligence algorithm combined with ultrasound endoscopy, and by comparing the real-time diagnosis of endoscopy and the ultrasonic image characteristics of artificial intelligence algorithm combined with endoscopic detection and pathological results. 120 cases were selected. According to the inclusion and exclusion criteria, 80 patients who met the criteria were selected and randomly divided into two groups: endoscopic examination combined with ultrasound imaging based on intelligent algorithm processing (cascade region-convolutional neural network (Cascade RCNN) model algorithm group) and simple use of endoscopy group (control group). This study shows that the ultrasonic image of artificial intelligence algorithm is effective, and the detection performance is better than that of endoscopic detection. The results are close to the gold standard of doctor recognition, and the detection time is greatly shortened, and the recognition time is shortened by 71 frames per second. Compared with the traditional convolutional neural network (CNN) algorithm, the accuracy and recall of image analysis and segmentation using feature pyramid network are increased. The detection rates of CNN model, Cascade RCNN model, and endoscopic detection alone in early esophageal cancer and precancerous lesions are 56.3% (45/80), 88.8% (71/80), and 44.1% (35/80), respectively. The detection rate of Cascade RCNN model and CNN model was higher than that of endoscopy alone, and the difference was statistically significant (P < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of Cascade RCNN model were higher than those of CNN model, which was close to the gold standard for physician identification. This provided a reference basis for endoscopic ultrasound identification of early upper gastrointestinal cancer or other gastrointestinal cancers.
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Gastrointestinal Disease Classification in Endoscopic Images Using Attention-Guided Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311136] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.
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42
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The emerging role of artificial intelligence in gastrointestinal endoscopy: A review. GASTROENTEROLOGIA Y HEPATOLOGIA 2021; 45:492-497. [PMID: 34793895 DOI: 10.1016/j.gastrohep.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/15/2021] [Accepted: 11/07/2021] [Indexed: 11/19/2022]
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43
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Yang XX, Li Z, Shao XJ, Ji R, Qu JY, Zheng MQ, Sun YN, Zhou RC, You H, Li LX, Feng J, Yang XY, Li YQ, Zuo XL. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig Endosc 2021; 33:1075-1084. [PMID: 33275789 DOI: 10.1111/den.13908] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Endoscopic diagnosis of early esophageal squamous cell cancer (ESCC) is complicated and dependent on operators' experience. This study aimed to develop an artificial intelligence (AI) model for automatic diagnosis of early ESCC. METHODS Non-magnifying and magnifying endoscopic images of normal/noncancerous lesions, early ESCC, and advanced esophageal cancer (AEC) were retrospectively obtained from Qilu Hospital of Shandong University. A total of 10,988 images from 5075 cases were chosen for training and validation. Another 2309 images from 1055 cases were collected for testing. One hundred and four real-time videos were also collected to evaluate the diagnostic performance of the AI model. The diagnostic performance of the AI model was compared with endoscopists by magnifying images and the assistant efficiency of the AI model for novices was evaluated. RESULTS The AI diagnosis for non-magnifying images showed a per-patient accuracy, sensitivity, and specificity of 99.5%, 100%, 99.5% for white light imaging, and 97.0%, 97.2%, 96.4% for optical enhancement/iodine straining images. Regarding diagnosis for magnifying images, the per-patient accuracy, sensitivity, and specificity were 88.1%, 90.9%, and 85.0%. The diagnostic accuracy of the AI model was similar to experts (84.5%, P = 0.205) and superior to novices (68.5%, P = 0.005). The diagnostic performance of novices was significantly improved by AI assistance. When it comes to the diagnosis for real-time videos, the AI model showed acceptable performance as well. CONCLUSIONS The AI model could accurately recognize early ESCC among noncancerous mucosa and AEC. It could be a potential assistant for endoscopists, especially for novices.
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Affiliation(s)
- Xiao-Xiao Yang
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xue-Jun Shao
- Qingdao Medicon Digital Engineering Co. Ltd, Qingdao, China
| | - Rui Ji
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI tumor, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jun-Yan Qu
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Meng-Qi Zheng
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi-Ning Sun
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ru-Chen Zhou
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hang You
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Li-Xiang Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jian Feng
- Qingdao Medicon Digital Engineering Co. Ltd, Qingdao, China
| | - Xiao-Yun Yang
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yan-Qing Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiu-Li Zuo
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI tumor, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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Li N, Jin SZ. Artificial intelligence and early esophageal cancer. Artif Intell Gastrointest Endosc 2021; 2:198-210. [DOI: 10.37126/aige.v2.i5.198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/23/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.
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Affiliation(s)
- Ning Li
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Shi-Zhu Jin
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2:124-132. [DOI: 10.35712/aig.v2.i5.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 06/26/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
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Wu L, Shang R, Sharma P, Zhou W, Liu J, Yao L, Dong Z, Yuan J, Zeng Z, Yu Y, He C, Xiong Q, Li Y, Deng Y, Cao Z, Huang C, Zhou R, Li H, Hu G, Chen Y, Wang Y, He X, Zhu Y, Yu H. Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial. Lancet Gastroenterol Hepatol 2021; 6:700-708. [PMID: 34297944 DOI: 10.1016/s2468-1253(21)00216-8] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/02/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasms. However, gastric neoplasms can be missed during upper gastrointestinal endoscopy due to the subtle nature of these lesions and varying skill among endoscopists. Here, we aimed to evaluate the effect of an artificial intelligence (AI) system designed to detect focal lesions and diagnose gastric neoplasms on reducing the miss rate of gastric neoplasms in clinical practice. METHODS This single-centre, randomised controlled, tandem trial was done at Renmin Hospital of Wuhan University, China. We recruited consecutive patients (≥18 years old) undergoing routine upper gastrointestinal endoscopy for screening, surveillance, or investigation of symptoms. Same-day tandem upper gastrointestinal endoscopy was done where patients first underwent either AI-assisted (AI-first) or routine (routine-first) white light endoscopy, followed immediately by the other procedure, with targeted biopsies for all detected lesions taken at the end of the second examination. Patients were randomly assigned (1:1) to the AI-first or routine-first group using a computer-generated random numerical series and block randomisation (block size of four). Endoscopists were not blinded to randomisation status, whereas patients and pathologists were. The primary endpoint was the miss rate of gastric neoplasms and the analysis was done per protocol. This trial is registered with the Chinese Clinical Trial Registry, ChiCTR2000034453, and has been completed. FINDINGS Between July 6, 2020, and Dec 11, 2020, 907 patients were randomly assigned to the AI-first group and 905 to the routine-first group. The gastric neoplasm miss rate was significantly lower in the AI-first group than in the routine-first group (6·1%, 95% CI 1·6-17·9 [3/49] vs 27·3%, 15·5-43·0 [12/44]; relative risk 0·224, 95% CI 0·068-0·744; p=0·015). The only reported adverse event was bleeding from a target lesion after biopsy. INTERPRETATION The use of an AI system during upper gastrointestinal endoscopy significantly reduced the gastric neoplasm miss rate. AI-assisted endoscopy has the potential to improve the yield of gastric neoplasms by endoscopists. FUNDING The Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision and the Hubei Province Major Science and Technology Innovation Project.
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical Center, Kansas City, MO, USA
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhi Zeng
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuanjie Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Chunping He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Qiutang Xiong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Yunchao Deng
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Zhuo Cao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Chao Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Rui Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Hongyan Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Guiying Hu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Yiyun Chen
- School of Resources and Environmental Sciences of Wuhan University, Wuhan, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, 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.
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Weng CY, Xu JL, Sun SP, Wang KJ, Lv B. Helicobacter pylori eradication: Exploring its impacts on the gastric mucosa. World J Gastroenterol 2021; 27:5152-5170. [PMID: 34497441 PMCID: PMC8384747 DOI: 10.3748/wjg.v27.i31.5152] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/14/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023] Open
Abstract
Helicobacter pylori (H. pylori) infects approximately 50% of all humans globally. Persistent H. pylori infection causes multiple gastric and extragastric diseases, indicating the importance of early diagnosis and timely treatment. H. pylori eradication produces dramatic changes in the gastric mucosa, resulting in restored function. Consequently, to better understand the importance of H. pylori eradication and clarify the subsequent recovery of gastric mucosal functions after eradication, we summarize histological, endoscopic, and gastric microbiota changes to assess the therapeutic effects on the gastric mucosa.
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Affiliation(s)
- Chun-Yan Weng
- Department of Gastroenterology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
| | - Jing-Li Xu
- Department of Gastrointestinal Surgery, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
| | - Shao-Peng Sun
- Department of Gastroenterology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
| | - Kai-Jie Wang
- Department of Gastroenterology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
| | - Bin Lv
- Department of Gastroenterology, The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, Zhejiang Province, China
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Sun X, Xu Y, Zhang X, Ma C, Li A, Yu H, Zhang W, Zhang H, Yang T, Miao X, Zhang H, Liu Y, Lu Z. Simethicone administration improves gastric cleanness for esophagogastroduodenoscopy: a randomized clinical trial. Trials 2021; 22:555. [PMID: 34419109 PMCID: PMC8379843 DOI: 10.1186/s13063-021-05527-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/09/2021] [Indexed: 12/16/2022] Open
Abstract
Background Esophagogastroduodenoscopy is very useful in diagnosing and treating upper gastrointestinal mucosal disorders, but too much foam and water in stomach decrease its diagnostic efficiency. Simethicone administration can help remove excessive foam. Aims To determine the optimal simethicone administration strategies in a comparative randomized controlled clinical trial. Methods Adult outpatients with indications for esophagogastroduodenoscopy were enrolled and randomly divided into group 1 (simethicone solution intake 20–30 min before procedure, n = 110), group 2 (simethicone solution intake 31–60 min before procedure, n = 92), and group 3 (simethicone solution intake > 60 min before procedure). Primary and secondary outcomes were procedure time and the patients’ satisfaction after the examination. All symptoms like abdominal pain and distension were recorded. Results No statistically significant differences were found on the patients’ demographic and clinical features and mean examination time (all P values > 0.05). The distribution of patients with different endoscopic and pathological diagnosis was comparable among three groups, respectively (P = 0.607; P = 0.289). However, the proportion of patients with Gastric Cleanness Grade A was most in group 2 (n = 73, 79.3%), and patient proportion with Gastric Cleanness Grade C was most found in group 1 (n = 72, 65.5%), which was greatly different (P < 0.001). There was no statistically significant difference on the satisfaction scores [immediately 6 (3–8) vs. 6 (1–10) vs. 6 (1-9), P = 0.533; 2 h after 10 (8–10) vs. 10 (10–10) vs. 10 (8-10), P = 0.463]. Conclusion Simethicone solution intake 31–60 min before esophagogastroduodenoscopy can help obtain the best gastric cleanness, which is recommended in clinical practice (registered at ClinicalTrials.gov, NCT03776916 on December 13, 2018).
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Affiliation(s)
- Xiaotian Sun
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China.,Department of Internal Medicine, Beijing South Medical District, Chinese PLA General Hospital, Beijing, 100161, China
| | - Yang Xu
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Xueting Zhang
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Cuiyun Ma
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Aitong Li
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Haiyan Yu
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Weihua Zhang
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Hanqing Zhang
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Teng Yang
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Xinfang Miao
- Clinic of Fuxing Road, Beijing South Medical District, Chinese PLA General Hospital, Beijing, 100161, China
| | - Huiming Zhang
- Clinic of Fuxing Road, Beijing South Medical District, Chinese PLA General Hospital, Beijing, 100161, China
| | - Yan Liu
- Gastrointestinal Endoscopy Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Zheng Lu
- Department of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, No. 100 Middle Road in Fourth West Ring, Beijing, 100039, China.
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50
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Lu YF, Lyu B. Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection. Artif Intell Gastrointest Endosc 2021; 2:50-62. [DOI: 10.37126/aige.v2.i3.50] [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: 05/02/2021] [Revised: 06/01/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
With the appearance and prevalence of deep learning, artificial intelligence (AI) has been broadly studied and made great progress in various fields of medicine, including gastroenterology. Helicobacter pylori (H. pylori), closely associated with various digestive and extradigestive diseases, has a high infection rate worldwide. Endoscopic surveillance can evaluate H. pylori infection situations and predict the risk of gastric cancer, but there is no objective diagnostic criteria to eliminate the differences between operators. The computer-aided diagnosis system based on AI technology has demonstrated excellent performance for the diagnosis of H. pylori infection, which is superior to novice endoscopists and similar to skilled. Compared with the visual diagnosis of H. pylori infection by endoscopists, AI possesses voluminous advantages: High accuracy, high efficiency, high quality control, high objectivity, and high-effect teaching. This review summarizes the previous and recent studies on AI-assisted diagnosis of H. pylori infection, points out the limitations, and puts forward prospect for future research.
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Affiliation(s)
- Yi-Fan Lu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, Zhejiang Province, China
| | - Bin Lyu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, Zhejiang Province, China
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