{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T01:05:40Z","timestamp":1793495140703,"version":"4.5.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Youth Program of Suzhou Health Committee","award":["KJXW2019001"],"award-info":[{"award-number":["KJXW2019001"]}]},{"name":"Wujiang Health Committee","award":["WWK202513"],"award-info":[{"award-number":["WWK202513"]}]},{"name":"Suzhou Clinical Center of Digestive Diseases","award":["Szlcyxzx202101"],"award-info":[{"award-number":["Szlcyxzx202101"]}]},{"name":"Frontier Technologies of Science and Technology Projects of Changzhou Municipal Health Commission","award":["QY202309"],"award-info":[{"award-number":["QY202309"]}]},{"name":"the Open Fund of Key Laboratory of Hepatosplenic Surgery, Ministry of Education","award":["GPKF202304"],"award-info":[{"award-number":["GPKF202304"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82000540"],"award-info":[{"award-number":["82000540"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Int Med Res"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a multimodal predictive model for the risk of strangulation in adhesive small bowel obstruction by integrating deep learning\u2013based computed tomography imaging features and clinical electronic health records.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective, observational, multicenter study was conducted across three hospitals, with data from 225 patients used for model development and 123 patients for external validation. A three-dimensional convolutional neural network with a ResNet50 backbone was used to segment abdominal regions from computed tomography scans and classify strangulation risk. The multimodal model integrated deep learning predictions with top electronic health record features using the XGBoost algorithm; global and local interpretability were achieved through variable importance ranking and local interpretable model-agnostic explanations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The multimodal model demonstrated superior performance in predicting strangulation within 7 days of admission, achieving an area under the curve of 0.915 in the training set and 0.912 in the test set, outperforming single-modality models. Calibration plots showed good alignment between predicted and observed outcomes, decision curve analysis demonstrated significant clinical utility, and net reclassification improvement confirmed that deep learning enhanced the model\u2019s predictive ability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study highlights the potential of multimodal artificial intelligence combined with clinical data to improve diagnostic accuracy and support clinical decision making in adhesive small bowel obstruction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/03000605251378951","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T22:30:58Z","timestamp":1758580258000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multimodal predictive model for strangulation risk in adhesive small bowel obstruction using deep learning and electronic health record data"],"prefix":"10.1177","volume":"53","author":[{"given":"Han","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Suzhou Yongding Hospital, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Suzhou Yongding Hospital, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianglin","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Suzhou Yongding Hospital, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaocheng","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Suzhou Yongding Hospital, 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