{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:13:44Z","timestamp":1787015624679,"version":"build-2736575974"},"reference-count":24,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Discrimination of gastrointestinal tuberculosis (GITB) and Crohn's disease (CD) is difficult. Use of artificial intelligence (AI)\u2010based technologies may help in discriminating these two entities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic review on the use of AI for discrimination of GITB and CD. Electronic databases (PubMed and Embase) were searched on June 6, 2022, to identify relevant studies. We included any study reporting the use of clinical, endoscopic, and radiological information (textual or images) to discriminate GITB and CD using any AI technique. Quality of studies was assessed with MI\u2010CLAIM checklist.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 27 identified results, a total of 9 studies were included. All studies used retrospective databases. There were five studies of only endoscopy\u2010based AI, one of radiology\u2010based AI, and three of multiparameter\u2010based AI. The AI models performed fairly well with high accuracy ranging from 69.6\u2013100%. Text\u2010based convolutional neural network was used in three studies and Classification and regression tree analysis used in two studies. Interestingly, irrespective of the AI method used, the performance of discriminating GITB and CD did not match in discriminating from other diseases (in studies where a third disease was also considered).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The use of AI in differentiating GITB and CD seem to have acceptable accuracy but there were no direct comparisons with traditional multiparameter models. The use of multiple parameter\u2010based AI models have the potential for further exploration in search of an ideal tool and improve on the accuracy of traditional models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.16430","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T19:00:03Z","timestamp":1701975603000},"page":"422-430","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Artificial intelligence for discrimination of Crohn's disease and gastrointestinal tuberculosis: A systematic review"],"prefix":"10.1111","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1701-6829","authenticated-orcid":false,"given":"Anurag","family":"Sachan","sequence":"first","affiliation":[{"name":"Department of Gastroenterology Postgraduate Institute of Medical Education and Research  Chandigarh 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