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We aimed to evaluate the ability of aMAP score for metabolic dysfunction\u2010associated steatotic liver disease (MASLD; formerly NAFLD)\u2010related fibrosis diagnosis and establish a machine\u2010learning (ML) model to improve the diagnostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 946 biopsy\u2010proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic\/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009703). The performance of ML models was evaluated in the external validation set (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009125).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The AUROCs of aMAP versus fibrosis\u20104 index (FIB\u20104) and aspartate aminotransferase\u2010platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.734], 0.735 [\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.001]) and (0.759 vs. 0.795 [\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.027], 0.709 [\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.049]). When using dual cut\u2010off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB\u20104 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM\u2010plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM\u2010plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM\u2010plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The aMAP score is capable of diagnosing MASLD\u2010related fibrosis. The LSM\u2010plus model could accurately identify MASLD\u2010related cirrhosis and advanced fibrosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.15818","type":"journal-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T06:12:39Z","timestamp":1703225559000},"page":"749-759","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Machine\u2010learning model comprising five clinical indices and\u00a0liver stiffness measurement can accurately identify\n                    <scp>MASLD<\/scp>\n                    \u2010related liver fibrosis"],"prefix":"10.1111","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-483X","authenticated-orcid":false,"given":"Rong","family":"Fan","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Viral Hepatitis Research Guangdong Provincial Clinical Research Center for Viral Hepatitis Key Laboratory of Infectious Diseases Research in South China Ministry of Education Department of Infectious Diseases Nanfang Hospital Southern Medical University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Yu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Viral Hepatitis Research Guangdong Provincial Clinical Research Center for Viral Hepatitis Key Laboratory of Infectious Diseases Research in South China Ministry of Education Department of Infectious Diseases Nanfang Hospital Southern Medical University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanlin","family":"Li","sequence":"additional","affiliation":[{"name":"Medical Data Analytics Center Department of Medicine and Therapeutics The Chinese University of Hong Kong  Hong Kong China"},{"name":"State Key Laboratory of Digestive Disease Institute of Digestive Disease The Chinese University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamoore","family":"Arshad","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Virginia Commonwealth University  Richmond Virginia United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen\u2010Yue","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology The First Affiliated Hospital of Wenzhou Medical University  Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2863-9389","authenticated-orcid":false,"given":"Grace Lai\u2010Hung","family":"Wong","sequence":"additional","affiliation":[{"name":"Medical Data Analytics Center Department of Medicine and Therapeutics The Chinese University of Hong Kong  Hong Kong China"},{"name":"State Key Laboratory of Digestive Disease Institute of Digestive Disease The Chinese University of Hong Kong  Hong Kong 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