{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T19:36:25Z","timestamp":1782502585123,"version":"3.54.5"},"reference-count":22,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100018254","name":"Japanese Foundation for Research and Promotion of Endoscopy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018254","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":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Adequate bowel preparation is crucial for high\u2010quality colonoscopy; however, assessing preparation adequacy can be burdensome for both healthcare providers and patients. In this study, we aimed to develop artificial intelligence (AI) models for the automated identification of bowel PREParation for cOlonoscOpy (AI\u2010PREPOO).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>On the day of colonoscopy, participants were instructed to use smartphones to photograph their stool in the toilet after each bowel movement following initiation of polyethylene glycol solution and upload the images to a secure web server. All images were labeled as \u201cready\u201d or \u201cnot ready\u201d for colonoscopy based on clarity and the absence of solid content. Using these labeled images, four image\u2010recognition models based on different deep learning architectures (AI\u2010PREPOO 1\u20134) were developed using transfer learning to classify stool status as \u201cready\u201d or \u201cnot ready.\u201d<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 282 stool images were collected from 37 patients, with 141 images labeled as \u201cready\u201d and 141 as \u201cnot ready.\u201d These images were divided into training (224 images) and test (58 images) sets, and the training set was augmented to 2240 images. All models trained on the augmented dataset achieved high performance, with area under the curve (AUC) values exceeding 0.90. AI\u2010PREPOO 1, based on MobileNetV3\u2010Small, demonstrated the most balanced sensitivity\u2013specificity profile (AUC, 0.95; sensitivity, 0.93; specificity, 0.86).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed AI\u2010based models capable of accurately assessing bowel preparation adequacy. AI\u2010PREPOO 1 showed a well\u2010balanced diagnostic performance, suggesting its potential to facilitate bowel preparation assessment and reduce the burden on healthcare providers and patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.70235","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:20:29Z","timestamp":1768209629000},"page":"678-685","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence Model for Automated Identification of Bowel Preparation for Colonoscopy (AI\u2010PREPOO): A Multicenter Study"],"prefix":"10.1111","volume":"41","author":[{"given":"Kosuke","family":"Kojima","sequence":"first","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences Niigata University  Niigata Japan"},{"name":"Division of Gastroenterology and Hepatology Niigata Prefectural Shibata Hospital  Niigata Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3097-9841","authenticated-orcid":false,"given":"Kazuya","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences Niigata University  Niigata Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Maruyama","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences Niigata University  Niigata Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nao","family":"Nakajima","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences Niigata University  Niigata Japan"},{"name":"Division of Gastroenterology and Hepatology Gosen Central Hospital  Niigata 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