{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:19:53Z","timestamp":1790659193333,"version":"4.1.0"},"reference-count":18,"publisher":"Georg Thieme Verlag KG","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Endoscopy"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>\n            Background\u2002Further development of deep learning-based artificial intelligence (AI) technology to automatically diagnose multiple abnormalities in small-bowel capsule endoscopy (SBCE) videos is necessary. We aimed to develop an AI model, to compare its diagnostic performance with doctors of different experience levels, and to further evaluate its auxiliary role for doctors in diagnosing multiple abnormalities in SBCE videos.<\/jats:p><jats:p>\n            Methods\u2002The AI model was trained using 280\u200a426 images from 2565 patients, and the diagnostic performance was validated in 240 videos.<\/jats:p><jats:p>\n            Results\u2002The sensitivity of the AI model for red spots, inflammation, blood content, vascular lesions, protruding lesions, parasites, diverticulum, and normal variants was 97.8\u200a%, 96.1\u200a%, 96.1\u200a%, 94.7\u200a%, 95.6\u200a%, 100\u200a%, 100\u200a%, and 96.4\u200a%, respectively. The specificity was 86.0\u200a%, 75.3\u200a%, 87.3\u200a%, 77.8\u200a%, 67.7\u200a%, 97.5\u200a%, 91.2\u200a%, and 81.3\u200a%, respectively. The accuracy was 95.0\u200a%, 88.8\u200a%, 89.2\u200a%, 79.2\u200a%, 80.8\u200a%, 97.5\u200a%, 91.3\u200a%, and 93.3\u200a%, respectively. For junior doctors, the assistance of the AI model increased the overall accuracy from 85.5\u200a% to 97.9\u200a% (P \u200a&lt;\u200a0.001, Bonferroni corrected), comparable to that of experts (96.6\u200a%, P\u200a&gt;\u200a0.0125, Bonferroni corrected).<\/jats:p><jats:p>\n            Conclusions\u2002This well-trained AI diagnostic model automatically diagnosed multiple small-bowel abnormalities simultaneously based on video-level recognition, with potential as an excellent auxiliary system for less-experienced endoscopists.<\/jats:p>","DOI":"10.1055\/a-1881-4209","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T22:36:58Z","timestamp":1659739018000},"page":"44-51","source":"Crossref","is-referenced-by-count":34,"title":["Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy"],"prefix":"10.1055","volume":"55","author":[{"given":"Zhen","family":"Ding","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiying","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ankon Technologies (Wuhan) Co., Ltd, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ankon Technologies (Wuhan) Co., Ltd, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6133-9062","authenticated-orcid":false,"given":"Shuxin","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"},{"name":"Department of Gastroenterology, the First Affiliated Hospital of Shihezi University School of Medicine, Shihezi 832008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sicheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanhua","family":"Ming","sequence":"additional","affiliation":[{"name":"Ankon Technologies (Wuhan) Co., Ltd, Wuhan, 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