{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:29:27Z","timestamp":1786580967523,"version":"build-2736575974"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":71,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Science and Technology Project of Huzhou City, Zhejiang Province","award":["No. 2023GY33"],"award-info":[{"award-number":["No. 2023GY33"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Gastroenterol"],"DOI":"10.1186\/s12876-026-04906-2","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T08:37:11Z","timestamp":1780475831000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"26","author":[{"given":"Luying","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiwang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingnan","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangpeng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hupo","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieqiong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"4906_CR1","doi-asserted-by":"publisher","first-page":"S32","DOI":"10.3350\/cmh.2022.0365","volume":"29","author":"ML Teng","year":"2023","unstructured":"Teng ML, Ng CH, Huang DQ, Chan KE, Tan DJ, Lim WH, et al. Global incidence and prevalence of nonalcoholic fatty liver disease. Clin Mol Hepatol. 2023;29:S32-s42.","journal-title":"Clin Mol Hepatol"},{"key":"4906_CR2","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s11154-021-09681-x","volume":"23","author":"Y Li","year":"2022","unstructured":"Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease (NAFLD): a systematic review. Rev Endocr Metab Disord. 2022;23:387\u2013400.","journal-title":"Rev Endocr Metab Disord"},{"key":"4906_CR3","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1016\/j.dld.2021.07.016","volume":"53","author":"M Aggarwal","year":"2021","unstructured":"Aggarwal M, Rozenbaum D, Bansal A, Garg R, Bansal P, McCullough A. Development of machine learning model to detect fibrotic non-alcoholic steatohepatitis in patients with non-alcoholic fatty liver disease. Dig Liver Dis. 2021;53:1669\u201372.","journal-title":"Dig Liver Dis"},{"issue":"11","key":"4906_CR4","doi-asserted-by":"publisher","first-page":"5965","DOI":"10.3390\/ijms25115965","volume":"25","author":"T Mouskeftara","year":"2024","unstructured":"Mouskeftara T, Kalopitas G, Liapikos T, Arvanitakis K, Germanidis G, Gika H. Predicting non-alcoholic steatohepatitis: a lipidomics-driven machine learning approach. Int J Mol Sci. 2024;25(11):5965.","journal-title":"Int J Mol Sci"},{"key":"4906_CR5","doi-asserted-by":"publisher","first-page":"1460431","DOI":"10.3389\/fimmu.2024.1460431","volume":"15","author":"C Cao","year":"2024","unstructured":"Cao C, Liu W, Guo X, Weng S, Chen Y, Luo Y, et al. Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning. Front Immunol. 2024;15:1460431.","journal-title":"Front Immunol"},{"issue":"6","key":"4906_CR6","first-page":"600","volume":"11","author":"A Alizargar","year":"2024","unstructured":"Alizargar A, Chang YL, Alkhaleefah M, Tan TH. Precision non-alcoholic fatty liver disease (NAFLD) diagnosis: leveraging ensemble machine learning and gender insights for cost-effective detection. Bioengineering (Basel, Switzerland). 2024;11(6):600.","journal-title":"Bioengineering (Basel, Switzerland)"},{"key":"4906_CR7","doi-asserted-by":"publisher","first-page":"101506","DOI":"10.1016\/j.diabet.2023.101506","volume":"50","author":"J Bilson","year":"2024","unstructured":"Bilson J, Mantovani A, Byrne CD, Targher G. Steatotic liver disease, MASLD and risk of chronic kidney disease. Diabetes Metab. 2024;50:101506.","journal-title":"Diabetes Metab"},{"key":"4906_CR8","doi-asserted-by":"publisher","first-page":"101167","DOI":"10.1016\/j.molmet.2021.101167","volume":"50","author":"V Ajmera","year":"2021","unstructured":"Ajmera V, Loomba R. Imaging biomarkers of NAFLD, NASH, and fibrosis. Mol Metab. 2021;50:101167.","journal-title":"Mol Metab"},{"key":"4906_CR9","doi-asserted-by":"publisher","first-page":"4574","DOI":"10.2174\/1381612825666190117102111","volume":"24","author":"M Papatheodoridi","year":"2018","unstructured":"Papatheodoridi M, Cholongitas E. Diagnosis of non-alcoholic fatty liver disease (NAFLD): current concepts. Curr Pharm Des. 2018;24:4574\u201386.","journal-title":"Curr Pharm Des"},{"key":"4906_CR10","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1053\/j.gastro.2018.12.036","volume":"156","author":"L Castera","year":"2019","unstructured":"Castera L, Friedrich-Rust M, Loomba R. Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156:1264-81.e4.","journal-title":"Gastroenterology"},{"key":"4906_CR11","doi-asserted-by":"publisher","first-page":"6053","DOI":"10.3748\/wjg.v25.i40.6053","volume":"25","author":"G Ferraioli","year":"2019","unstructured":"Ferraioli G, Soares Monteiro LB. Ultrasound-based techniques for the diagnosis of liver steatosis. World J Gastroenterol. 2019;25:6053\u201362.","journal-title":"World J Gastroenterol"},{"key":"4906_CR12","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1007\/s10620-016-4037-1","volume":"61","author":"S Kinner","year":"2016","unstructured":"Kinner S, Reeder SB, Yokoo T. Quantitative imaging biomarkers of NAFLD. Dig Dis Sci. 2016;61:1337\u201347.","journal-title":"Dig Dis Sci"},{"issue":"7","key":"4906_CR13","doi-asserted-by":"publisher","first-page":"822","DOI":"10.3390\/metabo13070822","volume":"13","author":"KL Chiu","year":"2023","unstructured":"Chiu KL, Chen YD, Wang ST, Chang TH, Wu JL, Shih CM, et al. Exploring the potential performance of Fibroscan for predicting and evaluating metabolic syndrome using a feature selected strategy of machine learning. Metabolites. 2023;13(7):822.","journal-title":"Metabolites"},{"issue":"12","key":"4906_CR14","first-page":"748","volume":"9","author":"FM Alshagathrh","year":"2022","unstructured":"Alshagathrh FM, Househ MS. Artificial intelligence for detecting and quantifying fatty liver in ultrasound images: a systematic review. Bioengineering (Basel, Switzerland). 2022;9(12):748.","journal-title":"Bioengineering (Basel, Switzerland)"},{"key":"4906_CR15","doi-asserted-by":"publisher","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","volume":"69s","author":"P Hamet","year":"2017","unstructured":"Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69s:S36-s40.","journal-title":"Metabolism"},{"key":"4906_CR16","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11892-021-01423-2","volume":"21","author":"A Nomura","year":"2021","unstructured":"Nomura A, Noguchi M, Kometani M, Furukawa K, Yoneda T. Artificial intelligence in current diabetes management and prediction. Curr Diab Rep. 2021;21:61.","journal-title":"Curr Diab Rep"},{"key":"4906_CR17","first-page":"e078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ (Clinical research ed). 2024;385:e078378.","journal-title":"BMJ (Clinical research ed)"},{"key":"4906_CR18","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nmeth.4642","volume":"15","author":"D Bzdok","year":"2018","unstructured":"Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15:233\u20134.","journal-title":"Nat Methods"},{"key":"4906_CR19","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/s00345-024-05145-4","volume":"42","author":"L Shen","year":"2024","unstructured":"Shen L, An J, Wang N, Wu J, Yao J, Gao Y. Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis. World J Urol. 2024;42:464.","journal-title":"World J Urol"},{"key":"4906_CR20","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s12876-020-01585-5","volume":"21","author":"P Decharatanachart","year":"2021","unstructured":"Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol. 2021;21:10.","journal-title":"BMC Gastroenterol"},{"key":"4906_CR21","doi-asserted-by":"publisher","first-page":"53","DOI":"10.2147\/CLEP.S501445","volume":"17","author":"Y Sun","year":"2025","unstructured":"Sun Y, Hu D, Yu M, Liang SB, Zheng Y, Wang X, et al. Diagnostic accuracy of non-invasive diagnostic tests for nonalcoholic fatty liver disease: a systematic review and network meta-analysis. Clin Epidemiol. 2025;17:53\u201371.","journal-title":"Clin Epidemiol"},{"key":"4906_CR22","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.4254\/wjh.v13.i10.1417","volume":"13","author":"A Atsawarungruangkit","year":"2021","unstructured":"Atsawarungruangkit A, Laoveeravat P, Promrat K. Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database. World J Hepatol. 2021;13:1417\u201327.","journal-title":"World J Hepatol"},{"key":"4906_CR23","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1016\/j.numecd.2024.02.004","volume":"34","author":"T Cao","year":"2024","unstructured":"Cao T, Zhu Q, Tong C, Halengbieke A, Ni X, Tang J, et al. Establishment of a machine learning predictive model for non-alcoholic fatty liver disease: a longitudinal cohort study. Nutr Metab Cardiovasc Dis. 2024;34:1456\u201366.","journal-title":"Nutr Metab Cardiovasc Dis"},{"key":"4906_CR24","doi-asserted-by":"publisher","first-page":"205520762412331","DOI":"10.1177\/20552076241233135","volume":"10","author":"C Chen","year":"2024","unstructured":"Chen C, Zhang WK, Yan GL, Tang CC. Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: an interpretable machine learning approach. Digit Health. 2024;10:20552076241233136.","journal-title":"Digit Health"},{"key":"4906_CR25","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1186\/s12889-024-19882-z","volume":"24","author":"J Deng","year":"2024","unstructured":"Deng J, Ji W, Liu H, Li L, Wang Z, Hu Y, et al. Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk. BMC Public Health. 2024;24:2545.","journal-title":"BMC Public Health"},{"key":"4906_CR26","doi-asserted-by":"publisher","first-page":"e18758","DOI":"10.1016\/j.heliyon.2023.e18758","volume":"9","author":"Y Deng","year":"2023","unstructured":"Deng Y, Ma Y, Fu J, Wang X, Yu C, Lv J, et al. A dynamic machine learning model for prediction of NAFLD in a health checkup population: a longitudinal study. Heliyon. 2023;9:e18758.","journal-title":"Heliyon"},{"key":"4906_CR27","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1007\/s00595-024-02822-0","volume":"54","author":"T Fujii","year":"2024","unstructured":"Fujii T, Iizawa Y, Kobayashi T, Hayasaki A, Ito T, Murata Y, et al. Radiomics-based prediction of nonalcoholic fatty liver disease following pancreatoduodenectomy. Surg Today. 2024;54:953\u201363.","journal-title":"Surg Today"},{"key":"4906_CR28","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1148\/radiol.2020191160","volume":"295","author":"A Han","year":"2020","unstructured":"Han A, Byra M, Heba E, Andre MP, Erdman JW Jr, Loomba R, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology. 2020;295:342\u201350.","journal-title":"Radiology"},{"key":"4906_CR29","doi-asserted-by":"publisher","first-page":"e46891","DOI":"10.2196\/46891","volume":"25","author":"G Huang","year":"2023","unstructured":"Huang G, Jin Q, Mao Y. Predicting the 5-year risk of nonalcoholic fatty liver disease using machine learning models: prospective cohort study. J Med Internet Res. 2023;25:e46891.","journal-title":"J Med Internet Res"},{"key":"4906_CR30","doi-asserted-by":"publisher","first-page":"846118","DOI":"10.3389\/fpubh.2022.846118","volume":"10","author":"W Ji","year":"2022","unstructured":"Ji W, Xue M, Zhang Y, Yao H, Wang Y. A Machine Learning-based framework to identify and classify non-alcoholic fatty liver disease in a large-scale population. Front Public Health. 2022;10:846118.","journal-title":"Front Public Health"},{"issue":"1","key":"4906_CR31","first-page":"8793659","volume":"2022","author":"Q Li","year":"2022","unstructured":"Li Q, Zhang X, Zhang C, Li Y, Zhang S. Risk factors and prediction models for nonalcoholic fatty liver disease based on random forest. Comput Math Methods Med. 2022;2022(1):8793659.","journal-title":"Comput Math Methods Med"},{"key":"4906_CR32","first-page":"1123","volume":"36","author":"C Liu","year":"2023","unstructured":"Liu C, Zhou SH, Su H, Yang WQ, Lu J. An Artificial Neural Network model combined with dietary retinol intake from different sources to predict the risk of nonalcoholic fatty liver disease. Biomed Environ Sci. 2023;36:1123\u201335.","journal-title":"Biomed Environ Sci"},{"key":"4906_CR33","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.hbpd.2021.08.004","volume":"20","author":"YX Liu","year":"2021","unstructured":"Liu YX, Liu X, Cen C, Li X, Liu JM, Ming ZY, et al. Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: an extended study. Hepatobiliary Pancreat Dis Int. 2021;20:409\u201315.","journal-title":"Hepatobiliary Pancreat Dis Int"},{"key":"4906_CR34","first-page":"12704","volume":"13","author":"X Ma","year":"2021","unstructured":"Ma X, Yang C, Liang K, Sun B, Jin W, Chen L, et al. A predictive model for the diagnosis of non-alcoholic fatty liver disease based on an integrated machine learning method. Am J Transl Res. 2021;13:12704\u201313.","journal-title":"Am J Transl Res"},{"key":"4906_CR35","doi-asserted-by":"publisher","first-page":"551","DOI":"10.34172\/aim.31269","volume":"27","author":"F Masaebi","year":"2024","unstructured":"Masaebi F, AzizmohammadLooha M, Mohammadzadeh M, Pahlevani V, Farjam M, Zayeri F, et al. Machine-learning application for predicting metabolic dysfunction-associated steatotic liver disease using laboratory and body composition indicators. Arch Iran Med. 2024;27:551\u201362.","journal-title":"Arch Iran Med"},{"key":"4906_CR36","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.hbpd.2023.03.009","volume":"22","author":"HY Peng","year":"2023","unstructured":"Peng HY, Duan SJ, Pan L, Wang MY, Chen JL, Wang YC, et al. Development and validation of machine learning models for nonalcoholic fatty liver disease. Hepatobiliary Pancreat Dis Int. 2023;22:615\u201321.","journal-title":"Hepatobiliary Pancreat Dis Int"},{"key":"4906_CR37","doi-asserted-by":"publisher","first-page":"3638","DOI":"10.1038\/s41598-023-30750-5","volume":"13","author":"S Qin","year":"2023","unstructured":"Qin S, Hou X, Wen Y, Wang C, Tan X, Tian H, et al. Machine learning classifiers for screening nonalcoholic fatty liver disease in general adults. Sci Rep. 2023;13:3638.","journal-title":"Sci Rep"},{"key":"4906_CR38","doi-asserted-by":"publisher","first-page":"20240","DOI":"10.1038\/s41598-021-99400-y","volume":"11","author":"P Sorino","year":"2021","unstructured":"Sorino P, Campanella A, Bonfiglio C, Mirizzi A, Franco I, Bianco A, et al. Development and validation of a neural network for NAFLD diagnosis. Sci Rep. 2021;11:20240.","journal-title":"Sci Rep"},{"key":"4906_CR39","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1080\/15257770.2022.2152046","volume":"42","author":"Y \u00dclger","year":"2023","unstructured":"\u00dclger Y, Delik A. Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks. Nucleosides Nucleotides Nucleic Acids. 2023;42:398\u2013406.","journal-title":"Nucleosides Nucleotides Nucleic Acids"},{"issue":"2","key":"4906_CR40","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1007\/s42000-025-00634-6","volume":"24","author":"Y Wang","year":"2025","unstructured":"Wang Y, Wang P. Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3. Hormones (Athens). 2025;24(2):461\u201376.","journal-title":"Hormones (Athens)"},{"key":"4906_CR41","doi-asserted-by":"publisher","first-page":"1450317","DOI":"10.3389\/fendo.2024.1450317","volume":"15","author":"B Yang","year":"2024","unstructured":"Yang B, Lu H, Ran Y. Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation. Front Endocrinol Lausanne. 2024;15:1450317.","journal-title":"Front Endocrinol Lausanne"},{"key":"4906_CR42","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1002\/jum.16194","volume":"42","author":"A Tahmasebi","year":"2023","unstructured":"Tahmasebi A, Wang S, Wessner CE, Vu T, Liu JB, Forsberg F, et al. Ultrasound-based machine learning approach for detection of nonalcoholic fatty liver disease. J Ultrasound Med. 2023;42:1747\u201356.","journal-title":"J Ultrasound Med"},{"issue":"5","key":"4906_CR43","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1007\/s10278-025-01394-w","volume":"38","author":"SY Chiang","year":"2025","unstructured":"Chiang SY, Wang YW, Su PY, Chang YY, Yen HH, Chang RF. PBCS-ConvNeXt: convolutional network-based automatic diagnosis of non-alcoholic fatty liver in abdominal ultrasound images. J Imaging Inform Med. 2025;38(5):2878\u201393.","journal-title":"J Imaging Inform Med"},{"key":"4906_CR44","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1111\/apt.14172","volume":"46","author":"TC Yip","year":"2017","unstructured":"Yip TC, Ma AJ, Wong VW, Tse YK, Chan HL, Yuen PC, et al. Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment Pharmacol Ther. 2017;46:447\u201356.","journal-title":"Aliment Pharmacol Ther"},{"key":"4906_CR45","doi-asserted-by":"publisher","first-page":"107932","DOI":"10.1016\/j.cmpb.2023.107932","volume":"244","author":"H Zamanian","year":"2024","unstructured":"Zamanian H, Shalbaf A, Zali MR, Khalaj AR, Dehghan P, Tabesh M, et al. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: a systematic review (2005-2023). Comput Methods Programs Biomed. 2024;244:107932.","journal-title":"Comput Methods Programs Biomed"},{"key":"4906_CR46","doi-asserted-by":"publisher","first-page":"175628482110628","DOI":"10.1177\/17562848211062807","volume":"14","author":"P Decharatanachart","year":"2021","unstructured":"Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis. Ther Adv Gastroenterol. 2021;14:17562848211062808.","journal-title":"Ther Adv Gastroenterol"},{"key":"4906_CR47","doi-asserted-by":"publisher","first-page":"e19645","DOI":"10.7717\/peerj.19645","volume":"13","author":"J Li","year":"2025","unstructured":"Li J, Chen J, Zeng X, Lyu G, Lin S, He S. Update of machine learning for ultrasound diagnosis of metabolic dysfunction-associated steatotic liver disease: a bright future for deep learning. PeerJ. 2025;13:e19645.","journal-title":"PeerJ"},{"key":"4906_CR48","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1007\/s13679-024-00574-z","volume":"13","author":"F Abdelhameed","year":"2024","unstructured":"Abdelhameed F, Kite C, Lagojda L, Dallaway A, Chatha KK, Chaggar SS, et al. Non-invasive scores and serum biomarkers for fatty liver in the era of metabolic dysfunction-associated steatotic liver disease (MASLD): a comprehensive review from NAFLD to MAFLD and MASLD. Curr Obes Rep. 2024;13:510\u201331.","journal-title":"Curr Obes Rep"},{"key":"4906_CR49","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/s11604-025-01930-2","volume":"44","author":"N Tamaki","year":"2026","unstructured":"Tamaki N, Kimura T, Wakabayashi SI, Yamazaki T, Iwadare T, Tanaka N, et al. Metabolic dysfunction-associated steatotic liver disease (MASLD): definition, diagnosis, natural history, and treatment. Jpn J Radiol. 2026;44:465\u201375.","journal-title":"Jpn J Radiol"},{"key":"4906_CR50","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/s10238-024-01553-3","volume":"25","author":"M Elsabaawy","year":"2025","unstructured":"Elsabaawy M, Naguib M, Abuamer A, Shaban A. Comparative application of MAFLD and MASLD diagnostic criteria on NAFLD patients: insights from a single-center cohort. Clin Exp Med. 2025;25:36.","journal-title":"Clin Exp Med"},{"key":"4906_CR51","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1038\/s42255-024-00985-1","volume":"6","author":"CL Hsu","year":"2024","unstructured":"Hsu CL, Loomba R. From NAFLD to MASLD: implications of the new nomenclature for preclinical and clinical research. Nat Metab. 2024;6:600\u20132.","journal-title":"Nat Metab"},{"key":"4906_CR52","doi-asserted-by":"publisher","first-page":"883","DOI":"10.21037\/hbsn-2025-404","volume":"14","author":"T Tsutsumi","year":"2025","unstructured":"Tsutsumi T, Kawaguchi T, Suzuki H, Hashida R, Fukunaga S, Nakano M, et al. Reconciling diagnostic definitions for proposing metabolic dysfunction-associated steatotic liver disease in chronic kidney disease risk. Hepatobiliary Surg Nutr. 2025;14:883\u20138.","journal-title":"Hepatobiliary Surg Nutr"},{"key":"4906_CR53","doi-asserted-by":"publisher","first-page":"e10605","DOI":"10.1097\/MD.0000000000010605","volume":"97","author":"X Wang","year":"2018","unstructured":"Wang X, Zhang X, Ma L. Diagnostic performance of magnetic resonance technology in detecting steatosis or fibrosis in patients with nonalcoholic fatty liver disease: a meta-analysis. Medicine. 2018;97:e10605.","journal-title":"Medicine"},{"key":"4906_CR54","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s12944-025-02544-3","volume":"24","author":"S Khanmohammadi","year":"2025","unstructured":"Khanmohammadi S, Fallahtafti P, Habibzadeh A, EzzatollahiTanha A, Alamdari AA, Fallahtafti P, et al. Effectiveness of body roundness index for the prediction of nonalcoholic fatty liver disease: a systematic review and meta-analysis. Lipids Health Dis. 2025;24:117.","journal-title":"Lipids Health Dis"},{"key":"4906_CR55","first-page":"3093","volume":"36","author":"S Parvandeh","year":"2020","unstructured":"Parvandeh S, Yeh HW, Paulus MP, McKinney BA. Consensus features nested cross-validation. Bioinformatics (Oxford, England). 2020;36:3093\u20138.","journal-title":"Bioinformatics (Oxford, England)"},{"key":"4906_CR56","first-page":"1083","volume":"42","author":"Y Liu","year":"2020","unstructured":"Liu Y, Liao S, Jiang S, Ding L, Lin H, Wang W. Fast cross-validation for kernel-based algorithms. IEEE Trans Pattern Anal Mach Intell. 2020;42:1083\u201396.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4906_CR57","doi-asserted-by":"publisher","first-page":"6628","DOI":"10.1109\/TNNLS.2021.3128173","volume":"34","author":"X Yang","year":"2023","unstructured":"Yang X, Wang Y, Wang R, Li J. Ensemble feature selection with block-regularized m \u00d7 2 cross-validation. IEEE Trans Neural Netw Learn Syst. 2023;34:6628\u201341.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4906_CR58","doi-asserted-by":"publisher","first-page":"e2200047","DOI":"10.1002\/bimj.202200047","volume":"65","author":"N Ballout","year":"2023","unstructured":"Ballout N, Etievant L, Viallon V. On the use of cross-validation for the calibration of the adaptive lasso. Biom J. 2023;65:e2200047.","journal-title":"Biom J"},{"key":"4906_CR59","first-page":"e2","volume":"14","author":"S Khatami","year":"2026","unstructured":"Khatami S, Faghihi M, Irajian P, Jafari-Nakhjavanlou A, Khatami H, Khatami R, et al. Comparing machine learning models for predicting mortality after myocardial infarction: a systematic review and meta-analysis. Arch Acad Emerg Med. 2026;14:e2.","journal-title":"Arch Acad Emerg Med"},{"issue":"12","key":"4906_CR60","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.3390\/medicina61122237","volume":"61","author":"D A\u00f1ez","year":"2025","unstructured":"A\u00f1ez D, Conti G, Uriarte JJ, Serrano-Olmedo JJ, Mart\u00ednez-Murillo R, Casanova-Carvajal O. Artificial intelligence pipeline for mammography-based breast cancer detection: an integrated systematic review and large-scale experimental validation. Medicina (Kaunas). 2025;61(12):2237.","journal-title":"Medicina (Kaunas)"},{"key":"4906_CR61","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s11255-024-04257-5","volume":"57","author":"S Najdaghi","year":"2025","unstructured":"Najdaghi S, Davani DN, Shafie D, Alizadehasl A. Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis. Int Urol Nephrol. 2025;57:855\u201374.","journal-title":"Int Urol Nephrol"},{"key":"4906_CR62","doi-asserted-by":"publisher","first-page":"105659","DOI":"10.1016\/j.ijmedinf.2024.105659","volume":"193","author":"A Zobeiri","year":"2025","unstructured":"Zobeiri A, Rezaee A, Hajati F, Argha A, Alinejad-Rokny H. Post-cardiac arrest outcome prediction using machine learning: a systematic review and meta-analysis. Int J Med Informatics. 2025;193:105659.","journal-title":"Int J Med Informatics"},{"key":"4906_CR63","doi-asserted-by":"publisher","first-page":"1591523","DOI":"10.3389\/fmed.2025.1591523","volume":"12","author":"C Yin","year":"2025","unstructured":"Yin C, Zhang H, Du J, Zhu Y, Zhu H, Yue H. Artificial intelligence in imaging for liver disease diagnosis. Front Med (Lausanne). 2025;12:1591523.","journal-title":"Front Med (Lausanne)"},{"key":"4906_CR64","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/s11901-017-0368-4","volume":"16","author":"T Hardy","year":"2017","unstructured":"Hardy T, McPherson S. Imaging-based assessment of steatosis, inflammation and fibrosis in NAFLD. Current Hepatology Reports. 2017;16:298\u2013307.","journal-title":"Current Hepatology Reports"},{"key":"4906_CR65","doi-asserted-by":"publisher","first-page":"3398","DOI":"10.3748\/wjg.v28.i27.3398","volume":"28","author":"LL Cao","year":"2022","unstructured":"Cao LL, Peng M, Xie X, Chen GQ, Huang SY, Wang JY, et al. Artificial intelligence in liver ultrasound. World J Gastroenterol. 2022;28:3398\u2013409.","journal-title":"World J Gastroenterol"},{"key":"4906_CR66","doi-asserted-by":"publisher","first-page":"e1887","DOI":"10.61919\/jhrr.v5i10.1887","volume":"5","author":"A Muhammad","year":"2025","unstructured":"Muhammad A, Shanawar H. Role of artificial intelligence in revolutionizing the diagnosis of fatty liver disease. Journal of Health and Rehabilitation Research. 2025;5:e1887.","journal-title":"Journal of Health and Rehabilitation Research"},{"key":"4906_CR67","doi-asserted-by":"publisher","first-page":"34408","DOI":"10.1038\/s41598-025-17386-3","volume":"15","author":"V Nivethitha","year":"2025","unstructured":"Nivethitha V, Daniel RA, Debnath A, Dwarakanathan V, Jeer G, Kavipriya G. Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis. Sci Rep. 2025;15:34408.","journal-title":"Sci Rep"},{"key":"4906_CR68","doi-asserted-by":"publisher","first-page":"e53654","DOI":"10.2196\/53654","volume":"8","author":"L Zhang","year":"2024","unstructured":"Zhang L, Huang Y, Huang M, Zhao CH, Zhang YJ, Wang Y. Development of cost-effective fatty liver disease prediction models in a Chinese population: statistical and machine learning approaches. JMIR formative research. 2024;8:e53654.","journal-title":"JMIR formative research"},{"key":"4906_CR69","doi-asserted-by":"publisher","unstructured":"Njei B, Tanih DN, Al-Ajlouni YA, Boateng S, Nguefang GL, Kanmounye US. AI-based screening for at-risk MASLD and advanced fibrosis in the US population: a cost-effectiveness analysis. Dig Dis Sci. 2026. https:\/\/doi.org\/10.1007\/s10620-026-09811-y.","DOI":"10.1007\/s10620-026-09811-y"}],"container-title":["BMC Gastroenterology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12876-026-04906-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12876-026-04906-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12876-026-04906-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:04:01Z","timestamp":1786579441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12876-026-04906-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":69,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["4906"],"URL":"https:\/\/doi.org\/10.1186\/s12876-026-04906-2","relation":{},"ISSN":["1471-230X"],"issn-type":[{"value":"1471-230X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]},"assertion":[{"value":"15 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable. This study is a systematic review and meta-analysis of previously published studies and did not involve direct interaction with human participants or animals.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. The manuscript does not contain any individual person\u2019s data.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"505"}}