{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:08:44Z","timestamp":1788354524632,"version":"build-2803163510"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472718"],"award-info":[{"award-number":["82472718"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n \n Metabolic dysfunction\u2010associated steatotic liver disease (MASLD) is highly prevalent. Existing non\u2010invasive models for detecting fibrotic metabolic dysfunction\u2010associated steatohepatitis (MASH) perform poorly, partly due to discordant fibrosis\u2010inflammation relationships. We aimed to develop and validate a machine learning\u2010based stepwise strategy to improve identification of high\u2010risk MASLD (\n F<\/jats:italic>\n \u2009\u2265\u20092\u2009+\u2009NAS \u2265\u20095).\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A multicenter retrospective cohort study analysed 840 biopsy\u2010proven MASLD patients (after quality control from 934) across eight centres. Logistic regression and multi\u2010omics detection identified predictors for significant fibrosis (\n F<\/jats:italic>\n \u2009\u2265\u20092) and definite MASH (NAS \u2265\u20095). Patients were divided into a training cohort and a validation cohort based on the centre. Eight machine learning algorithms were trained to diagnose five endpoints. Four diagnostic strategies were compared: fibrosis\u2010first (\n F<\/jats:italic>\n \u2009\u2265\u20092 then NAS \u2265\u20095), MASH\u2010first (NAS \u2265\u20095 then\n F<\/jats:italic>\n \u2009\u2265\u20092), parallel (simultaneous\n F<\/jats:italic>\n \u2009\u2265\u20092 and NAS \u2265\u20095), and single\u2010model (direct\n F<\/jats:italic>\n \u2009\u2265\u20092 and NAS \u2265\u20095).\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Both logistic regression and mass spectrometry data from this study demonstrated differences in fibrosis and NAS scores. Optimal models were: random forest (RF) for\n F<\/jats:italic>\n \u2009\u2265\u20092, lightGBM for NAS \u2265\u20095, multilayer perceptron (MLP) for\n F<\/jats:italic>\n \u2009\u2265\u20092 & NAS \u2265\u20095 and NAS \u2265\u20095|\n F<\/jats:italic>\n \u2009\u2265\u20092, and elastic net for\n F<\/jats:italic>\n \u2009\u2265\u20092|NAS \u2265\u20095. Internal validation showed the fibrosis\u2010first strategy achieved superior performance (accuracy 84.7%, specificity 87.0%, NPV 92.3%). External validation confirmed stepwise approaches outperformed the single\u2010model strategy. The sequential RF (\n F<\/jats:italic>\n \u2009\u2265\u20092) followed by MLP (NAS \u2265\u20095) approach demonstrated the highest clinical utility.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n A machine learning\u2010based stepwise diagnostic strategy, prioritising fibrosis assessment first, significantly improves identification of high\u2010risk MASLD (\n F<\/jats:italic>\n \u2009\u2265\u20092\u2009+\u2009NAS \u2265\u20095). This validated approach enhances risk stratification accuracy, reduces reliance on biopsy, and offers strong clinical applicability for optimising management. Findings support integrating sequential AI diagnostics into clinical practice and future guidelines.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/liv.70491","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:02:27Z","timestamp":1766484147000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Two\u2010Step Machine Learning Model Enhances Identification of High\u2010Risk Metabolic Dysfunction\u2010Associated Steatotic Liver Disease"],"prefix":"10.1111","volume":"46","author":[{"given":"Zheng'ao","family":"Xu","sequence":"first","affiliation":[{"name":"Zhejiang Key Laboratory of Multi\u2010Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run\u2010Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"},{"name":"Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment Hangzhou China"},{"name":"Zhejiang University Cancer Center Hangzhou China"},{"name":"Liangzhu Laboratory Zhejiang University Medical Center Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongfen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Multi\u2010Omics Precision Diagnosis and Treatment of Liver Diseases, Department of Liver and Infectious Diseases, Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengjuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology The First Affiliated Hospital of Jinan University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiongyue","family":"Fan","sequence":"additional","affiliation":[{"name":"MAFLD Research Center, Department of Hepatology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China"},{"name":"Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Life Science Zhejiang Chinese Medical University Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Life Science Zhejiang Chinese Medical University Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xunxun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases The Affiliated Infectious Diseases Hospital of Soochow University Suzhou Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0953-5912","authenticated-orcid":false,"given":"Li","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases The Affiliated Infectious Diseases Hospital of Soochow University Suzhou Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fushuang","family":"Ha","sequence":"additional","affiliation":[{"name":"The Third Central Hospital of Tianjin Tianjin China"},{"name":"Central Hospital of Tianjin University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5817-0713","authenticated-orcid":false,"given":"Qing","family":"Ye","sequence":"additional","affiliation":[{"name":"The Third Central Hospital of Tianjin Tianjin China"},{"name":"Central Hospital of Tianjin University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3941-8426","authenticated-orcid":false,"given":"Chunyan","family":"Ye","sequence":"additional","affiliation":[{"name":"Institute of Hepatology The Third People's Hospital of Changzhou Changzhou Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Chen","sequence":"additional","affiliation":[{"name":"Well\u2010Healthcare Technologies, co., Ltd. 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