{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T19:08:19Z","timestamp":1778699299625,"version":"3.51.4"},"reference-count":34,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Hepatology Research"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>At\u2010risk metabolic dysfunction\u2010associated steatohepatitis (MASH), characterized by significant activity and fibrosis, increases the risk of liver complications. Liver stiffness measurement (LSM), commonly used to detect significant fibrosis, has limitations in terms of accessibility and performance in certain populations. We aimed to develop a simple\u2010to\u2010use machine learning (ML) model to identify at\u2010risk MASH without LSM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 884 patients with histologically confirmed metabolic dysfunction\u2010associated steatotic liver disease from a nationwide multicenter cohort, divided into derivation (80%) and validation (20%) sets. Multiple ML algorithms (random forest [RF], logistic regression [LR], gradient boosting [GB], support vector machine [SVM], and deep learning [DL]) were trained using variables including age, sex, body mass index, hematological\/biochemical parameters, and comorbidities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the validation cohort, the RF model showed superior discriminatory ability for predicting at\u2010risk MASH (AUROC: 0.8405) compared to LR (0.7729), GB (0.8252), SVM (0.7816), and DL (0.7422). Using only seven routine clinical parameters, the RF model outperformed the fibrosis\u20104 index (AUROC: 0.7329;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001) and LSM (AUROC: 0.7428;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.01) and showed comparable performance to the FibroScan\u2010aspartate aminotransferase score (AUROC: 0.7914;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.09) in the validation cohort. This RF model, termed the STEALTH\u2010ARMS model, was implemented as an online application to enable patient\u2010based individual risk assessment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The RF\u2010based ML model demonstrated high accuracy in identifying at\u2010risk MASH using only seven routine clinical data, offering a highly accurate, noninvasive, and cost\u2010effective alternative to LSM\u2010based methods. This approach holds promise for broader clinical applications, particularly in resource\u2010limited settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>UMIN\u2010CTR 000049068<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/hepr.70125","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:31:29Z","timestamp":1769189489000},"page":"753-761","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Approach Enables Highly Accurate Identification of At\u2010Risk Metabolic Dysfunction\u2010Associated Steatohepatitis"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5423-9664","authenticated-orcid":false,"given":"Masaya","family":"Sato","sequence":"first","affiliation":[{"name":"Department of Gastroenterology Graduate School of Medicine The University of Tokyo  Tokyo Japan"},{"name":"Department of Clinical Laboratory Medicine Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5727-5385","authenticated-orcid":false,"given":"Takuma","family":"Nakatsuka","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatsuya","family":"Minami","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1869-2810","authenticated-orcid":false,"given":"Yotaro","family":"Kudo","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5777-0140","authenticated-orcid":false,"given":"Rie","family":"Irie","sequence":"additional","affiliation":[{"name":"Department of Pathology Keio University School of Medicine  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanako","family":"Tsujikawa","sequence":"additional","affiliation":[{"name":"Department of Pathology Keio University School of Medicine  Tokyo Japan"},{"name":"Department of Diagnostic Pathology National Hospital Organization Saitama Hospital  Saitama Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Kawaguchi","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Department of Medicine Kurume University School of Medicine  Fukuoka 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