{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:05:13Z","timestamp":1789362313302,"version":"build-2803163510"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gangrenous cholecystitis (GC) is a severe surgical emergency with high postoperative morbidity and mortality, making early and accurate diagnosis essential for timely intervention. Conventional statistical models are limited in capturing nonlinear interactions among clinical variables and often lack interpretability, hindering individualized risk stratification. Machine learning enables accurate and interpretable risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The retrospective clinical study included a total of 492 patients with 39 routine serological tests and imaging tests collected. Seven models including LR, DT, RF, XGBoost, LightGBM, SVM and ANN were constructed. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and other metrics in train data, internal validation data and an independent external test data. An interpretable web-based risk calculator was dveloped based on the optimal model, with decile stratification employed to verify the reliability of predicted probabilities. SHapley Additive exPlanations (SHAP) were applied for feature interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The ANN model demonstrated the best performance, with an AUC of 0.932 (95% CI 0.900\u20130.961) in train data, 0.921 (95% CI 0.862\u20130.966) in internal validation data, and 0.867 (95% CI 0.790\u20130.929) in external validation. Five core predictors: NLR, WBC, FIB, NEU. and GBWT were identified as the most influential predictors. SHAP analysis provided interpretable insights into their contributions to infection risk. ANN model has been translated into a convenient tool to facilitate its utility in clinical settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed and externally validated an ANN-based prediction model for the early diagnosis of GC, achieving robust predictive performance and generalizability. The model holds promise for providing an early, personalized assessment of whether patients with cholecystitis will develop gangrene. Future prospective studies are warranted to further validate its efficacy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1930347","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T04:38:56Z","timestamp":1789360736000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and external validation of a machine learning model for predicting gangrenous cholecystitis"],"prefix":"10.3389","volume":"13","author":[{"given":"Xianyue","family":"Ren","sequence":"first","affiliation":[{"name":"Department of General Surgery, The First Affiliated Hospital of Jinzhou Medical University","place":["Jinzhou, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The First Affiliated Hospital of Jinzhou Medical University","place":["Jinzhou, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The First Affiliated Hospital of Jinzhou Medical University","place":["Jinzhou, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The First Affiliated Hospital of Jinzhou Medical University","place":["Jinzhou, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Cong","sequence":"additional","affiliation":[{"name":"Department of General Surgery, The First Affiliated Hospital of Jinzhou Medical University","place":["Jinzhou, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.14744\/tjtes.2021.50241","article-title":"Should the severity of acute cholecystitis (Tokyo 2018 guideline) affect the decision of early or delayed cholecystectomy?","volume":"28","author":"D\u00f6nder","year":"2022","journal-title":"Ulus Travma Acil Cerrahi Derg"},{"key":"B2","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":"B3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1002\/jhbp.512","article-title":"Tokyo Guidelines 2018: diagnostic criteria and severity grading of acute cholangitis (with videos)","volume":"25","author":"Kiriyama","year":"2018","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"B4","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":"B5","doi-asserted-by":"publisher","first-page":"254","DOI":"10.9738\/INTSURG-D-13-00222.1","article-title":"Gangrenous cholecystitis: mortality and risk factors","volume":"100","author":"\u00d6nder","year":"2015","journal-title":"Int Surg"},{"key":"B6","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/S0039-6060(99)70122-4","article-title":"Gangrenous cholecystitis: analysis of risk factors and experience with laparoscopic cholecystectomy","volume":"126","author":"Merriam","year":"1999","journal-title":"Surgery"},{"key":"B7","doi-asserted-by":"publisher","first-page":"228","DOI":"10.14701\/ahbps.2019.23.3.228","article-title":"Predictive factors for gangrene complication in acute calculous cholecystitis","volume":"23","author":"Shirah","year":"2019","journal-title":"Ann Hepatobiliary Pancreat Surg"},{"key":"B8","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s00423-025-03886-3","article-title":"Predicting the timing of LC after PTGBD in elderly patients with acute cholecystitis: a machine learning approach with a web-based calculator","volume":"411","author":"Zhang","year":"2025","journal-title":"Langenbecks Arch Surg"},{"key":"B9","doi-asserted-by":"publisher","first-page":"267","DOI":"10.33945\/SAMI\/IJABBR.2019.3.7","article-title":"Risk factors of gangrenous cholecystitis in patients with acute cholecystitis: a cross-sectional study","volume":"7","author":"Roozbahani","year":"2019","journal-title":"Int J Adv Biol Biomed Res"},{"key":"B10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.17235\/reed.2020.7282\/2020","article-title":"Can inflammatory biomarkers help in the diagnosis and prognosis of gangrenous acute cholecystitis? 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