{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:12:23Z","timestamp":1788415943452,"version":"build-2803163510"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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. Surg."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gangrenous cholecystitis (GC) is a severe pathological subtype of acute cholecystitis, yet its preoperative diagnostic rate remains below 10%. Conventional scoring systems based on logistic regression demonstrate limited predictive performance. Artificial intelligence (AI) approaches may offer improved risk stratification, but the evidence base remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A scoping review was conducted across PubMed, Web of Science, Embase, and CNKI databases for publications from January 2020 to June 2026. The search combined MeSH terms and free-text keywords related to gangrenous cholecystitis and artificial intelligence. After screening 412 records, 18 studies were included: 3 direct GC AI prediction studies, 3 traditional GC scoring systems, and 12 indirect or methodologically related studies. A narrative synthesis was adopted given the substantial heterogeneity in study design.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Three studies directly addressed AI-based GC prediction. Machine learning models using structured clinical data achieved validation AUCs of 0.818\u20130.944, though these were derived from retrospective, single-center or limited multicenter cohorts. Deep learning models integrating non-contrast and contrast-enhanced CT achieved independent validation AUCs of 0.879 and 0.887 (training-set AUC 0.965). Explainable AI methods identified model-associated predictors, including hypokalemia\/hyponatremia, though these require pathophysiological validation. No study reported calibration, decision curve analysis, or prospective clinical impact evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Early retrospective studies show promising discrimination for AI-based GC prediction; however, evidence remains insufficient for routine clinical decision-making due to methodological heterogeneity, limited external validation, absence of prospective impact studies, and lack of calibration and clinical utility analyses. Prospective multicenter validation and implementation research are needed before clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fsurg.2026.1934684","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:14:08Z","timestamp":1788412448000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in gangrenous cholecystitis: advances in risk factor identification and preoperative prediction\u2014a scoping review"],"prefix":"10.3389","volume":"13","author":[{"given":"Ping","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Hepatopancreatobiliary Surgery, Taizhou Fourth People\u2019s Hospital","place":["Taizhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hepatopancreatobiliary Surgery, Taizhou Fourth People\u2019s Hospital","place":["Taizhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianjiao","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Hepatopancreatobiliary Surgery, Taizhou Fourth People\u2019s Hospital","place":["Taizhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jisong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hepatopancreatobiliary Surgery, Taizhou Fourth People\u2019s Hospital","place":["Taizhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","first-page":"422","article-title":"Guidelines for the diagnosis and treatment of acute biliary tract infections (2021 edition)","volume":"59","year":"2021","journal-title":"Chinese Journal of Surgery"},{"key":"B2","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s12893-025-03356-z","article-title":"Predictors of acute gangrenous cholecystitis and its complications: a retrospective cohort study","volume":"26","author":"El Asmar","year":"2026","journal-title":"BMC Surg"},{"key":"B3","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.amjsurg.2003.08.001","article-title":"Prognostic factors for the development of gangrenous cholecystitis","volume":"186","author":"Fagan","year":"2003","journal-title":"Am J Surg"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e74126","DOI":"10.7759\/cureus.74126","article-title":"Risk factors associated with gangrenous cholecystitis: a cohort study from eastern India","volume":"16","author":"Karthikarajam","year":"2024","journal-title":"Cureus"},{"key":"B5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13017-020-00336-x","article-title":"2020 world society of emergency surgery updated guidelines for the diagnosis and treatment of acute calculus cholecystitis","volume":"15","author":"Pisano","year":"2020","journal-title":"World J Emerg Surg"},{"key":"B6","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1002\/jhbp.516","article-title":"Tokyo guidelines 2018: flowchart for the management of acute cholecystitis","volume":"25","author":"Okamoto","year":"2018","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"B7","doi-asserted-by":"publisher","first-page":"16","DOI":"10.21037\/tgh-24-27","article-title":"Enhancing the management of acute and gangrenous cholecystitis: a systematic review supported by the TriNetX database","volume":"10","author":"Fabbri","year":"2025","journal-title":"Transl Gastroenterol Hepatol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"147","DOI":"10.52787\/agl.v54i2.402","article-title":"Risk factors associated with acute gangrenous cholecystitis","volume":"54","author":"C\u00f3rdova-Vel\u00e1zquez","year":"2024","journal-title":"Acta Gastroenterol Latinoam"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1007\/s11605-020-04707-2","article-title":"Acute gangrenous cholecystitis: proposal of a score and comparison with previous published scores","volume":"25","author":"Bouassida","year":"2021","journal-title":"J Gastrointest Surg"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1186\/s40001-025-03196-w","article-title":"Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives","volume":"30","author":"Fahim","year":"2025","journal-title":"Eur J Med Res"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1644041","DOI":"10.3389\/fdgth.2025.1644041","article-title":"Artificial intelligence in healthcare: applications, challenges, and future directions. 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