{"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":1793715930942,"version":"4.6.1"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>Crohn\u2019s disease (CD), a complex member of the inflammatory bowel disease spectrum, is characterized by the diversity and skipping distribution of intestinal mucosal lesions, significantly complicating its differential diagnosis with intestinal diseases such as ulcerative colitis and intestinal tuberculosis. With the increasing application of artificial intelligence (AI) in the medical field, its utilization in primary diagnosis has become more widespread. However, there is a lack of systematic evaluation regarding the specific efficacy of AI in identifying CD through capsule endoscopy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study conducted a comprehensive search of PubMed databases, Cochrane, EMBASE, and Web of Science up to May 21, 2024, to collect relevant literature. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to rigorously assess the quality of included studies, and detailed information on study characteristics and AI algorithms was extracted. A bivariate mixed-effects model was employed to synthesize and analyze the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, meta-regression and subgroup analyses were conducted to delve into the potential sources of heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Ultimately, eight studies encompassing 11 distinct AI models were included in this meta-analysis. The overall area under the curve (AUC) for AI in identifying CD through capsule endoscopy was 99% (95% CI, 100%-0.00), indicating high diagnostic accuracy. Specifically, the pooled sensitivity was 94% (95% CI, 93\u201396%), specificity was 97% (95% CI, 95\u201398%), positive likelihood ratio (PLR) was 32.7 (95% CI, 19.9\u201353.6), negative likelihood ratio (NLR) was 6% (95% CI, 4\u20137%), and diagnostic odds ratio (DOR) reached 576 (95% CI, 295\u20131,127). Meta-regression analysis further revealed that AI algorithm type, study population size, and study design might be key sources of heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the significant potential of AI technology in assisting endoscopists in detecting and identifying CD patients through capsule endoscopy. However, given the limitations and heterogeneity of current research, more high-quality, large-sample studies are needed to comprehensively and thoroughly evaluate the practical application value of AI in CD diagnosis, thereby promoting its widespread adoption and optimization in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1531362","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T02:17:40Z","timestamp":1743473860000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial intelligence-assisted capsule endoscopy for detecting lesions in Crohn\u2019s disease: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"8","author":[{"given":"Yuling","family":"Bin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rumei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqian","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.1111\/jgh.15070","article-title":"The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: a systematic review with meta-analysis","volume":"35","author":"Aziz","year":"2020","journal-title":"J. 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