{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T14:25:30Z","timestamp":1793715930936,"version":"4.6.1"},"reference-count":34,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Science and Technology Plan (Apply Basic Research) of Changzhou City","award":["(CJ20210006)"],"award-info":[{"award-number":["(CJ20210006)"]}]},{"name":"Medical Education Collaborative Innovation Fund of Jiangsu University","award":["(JDY2022018)"],"award-info":[{"award-number":["(JDY2022018)"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Int Med Res"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605231200371","type":"journal-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T05:00:47Z","timestamp":1697000447000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep learning to predict esophageal variceal bleeding based on endoscopic images"],"prefix":"10.1177","volume":"51","author":[{"given":"Yu","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Jintan Affiliated Hospital of Jiangsu University, Jintan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minyue","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 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