{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T12:19:28Z","timestamp":1791634768720,"version":"4.3.4"},"reference-count":27,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:00:00Z","timestamp":1613779200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102018R1D1A1B07048179"],"award-info":[{"award-number":["NRF\u20102018R1D1A1B07048179"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI18C0094"],"award-info":[{"award-number":["HI18C0094"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep\u2010learning algorithm that performs differential diagnosis between intestinal Beh\u00e7et's disease (BD), Crohn's disease (CD), and intestinal tuberculosis (ITB) using colonoscopy images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The typical pattern for each disease was defined as a typical image. We implemented a convolutional neural network (CNN) using Pytorch and visualized a deep\u2010learning model through Gradient\u2010weighted Class Activation Mapping. The performance of the algorithm was evaluated using the area under the receiver operating characteristic curve (AUROC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 6617 colonoscopy images of 211 CD, 299 intestinal BD, and 217 ITB patients were used. The accuracy of the algorithm for discriminating the three diseases (all\u2010images: 65.15%\n                      <jats:italic>vs<\/jats:italic>\n                      typical images: 72.01%,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.024) and discriminating between intestinal BD and CD (all\u2010images: 78.15%\n                      <jats:italic>vs<\/jats:italic>\n                      typical images: 85.62%,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.010) was significantly different between all\u2010images and typical images. The CNN clearly differentiated colonoscopy images of the diseases (AUROC from 0.7846 to 0.8586). Algorithmic prediction AUROC for typical images ranged from 0.8211 to 0.9360.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study found that a deep\u2010learning model can discriminate between colonoscopy images of intestinal BD, CD, and ITB. In particular, the algorithm demonstrated superior discrimination ability for typical images. This approach presents a beneficial method for the differential diagnosis of the diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.15433","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T01:49:59Z","timestamp":1612748999000},"page":"2141-2148","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Deep\u2010learning system for real\u2010time differentiation between Crohn's disease, intestinal Beh\u00e7et's disease, and intestinal tuberculosis"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3910-9380","authenticated-orcid":false,"given":"Jung Min","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Internal Medicine and Institute of Gastroenterology Yonsei University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9275-3194","authenticated-orcid":false,"given":"Jun Gu","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Institute of Gastroenterology Yonsei University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5455-6926","authenticated-orcid":false,"given":"Sungwon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital Yonsei University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2282-8904","authenticated-orcid":false,"given":"Jae Hee","family":"Cheon","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine and Institute of Gastroenterology Yonsei University College of Medicine  Seoul Republic of Korea"},{"name":"Brain Korea 21 PLUS Project for Medical Science Yonsei University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02604297"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/ajg.2009.331"},{"key":"e_1_2_5_4_1","first-page":"161","article-title":"Diagnostic guideline of Crohn's disease","volume":"53","author":"Ye BD","year":"2009","journal-title":"Kor. 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