{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T03:52:03Z","timestamp":1791258723179,"version":"4.1.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:00:00Z","timestamp":1783728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":87,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Chongqing Hospital of Traditional Chinese Medicine","award":["CQEIC2024MDAD-042"],"award-info":[{"award-number":["CQEIC2024MDAD-042"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03638-3","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:44:53Z","timestamp":1783784693000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a machine learning model for identifying liver fibrosis in MAFLD: a retrospective model development study"],"prefix":"10.1186","volume":"26","author":[{"given":"Chenyu","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"3638_CR1","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.4254\/wjh.v15.i12.1253","volume":"15","author":"L Chen","year":"2023","unstructured":"Chen L. From metabolic dysfunction-associated fatty liver disease to metabolic dysfunction-associated steatotic liver disease: Controversy and consensus. World J Hepatol. 2023;15:1253\u20137. https:\/\/doi.org\/10.4254\/wjh.v15.i12.1253.","journal-title":"World J Hepatol"},{"key":"3638_CR2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ejim.2024.04.013","volume":"124","author":"P Portincasa","year":"2024","unstructured":"Portincasa P, Baffy G. Metabolic dysfunction-associated steatotic liver disease: Evolution of the final terminology. Eur J Intern Med. 2024;124:35\u20139. https:\/\/doi.org\/10.1016\/j.ejim.2024.04.013.","journal-title":"Eur J Intern Med"},{"key":"3638_CR3","doi-asserted-by":"publisher","first-page":"972","DOI":"10.14218\/JCTH.2021.00201","volume":"9","author":"H Lin","year":"2021","unstructured":"Lin H, Zhang X, Li G, Wong GL-H, Wong VW-S. Epidemiology and Clinical Outcomes of Metabolic (Dysfunction)-associated Fatty Liver Disease. J Clin Transl Hepatol. 2021;9:972\u201382. https:\/\/doi.org\/10.14218\/JCTH.2021.00201.","journal-title":"J Clin Transl Hepatol"},{"key":"3638_CR4","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.ecl.2023.02.002","volume":"52","author":"K Venkatesan","year":"2023","unstructured":"Venkatesan K, Haroon NN. Management of Metabolic-Associated Fatty Liver Disease. Endocrinol Metab Clin North Am. 2023;52:547\u201357. https:\/\/doi.org\/10.1016\/j.ecl.2023.02.002.","journal-title":"Endocrinol Metab Clin North Am"},{"key":"3638_CR5","doi-asserted-by":"publisher","first-page":"257","DOI":"10.3350\/cmh.2021.0067","volume":"27","author":"SH Kang","year":"2021","unstructured":"Kang SH, Cho Y, Jeong SW, Kim SU, Lee J-W, Korean NAFLD Study Group. From nonalcoholic fatty liver disease to metabolic-associated fatty liver disease: Big wave or ripple? Clin Mol Hepatol. 2021;27:257\u201369. https:\/\/doi.org\/10.3350\/cmh.2021.0067.","journal-title":"Clin Mol Hepatol"},{"key":"3638_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1159\/000515445","volume":"37","author":"E Roeb","year":"2021","unstructured":"Roeb E. Excess Body Weight and Metabolic (Dysfunction)-Associated Fatty Liver Disease (MAFLD). Visc Med. 2021;37:273\u201380. https:\/\/doi.org\/10.1159\/000515445.","journal-title":"Visc Med"},{"key":"3638_CR7","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1586\/17474124.2016.1099433","volume":"10","author":"P Golabi","year":"2016","unstructured":"Golabi P, Sayiner M, Fazel Y, Koenig A, Henry L, Younossi ZM. Current complications and challenges in nonalcoholic steatohepatitis screening and diagnosis. Expert Rev Gastroenterol Hepatol. 2016;10:63\u201371. https:\/\/doi.org\/10.1586\/17474124.2016.1099433.","journal-title":"Expert Rev Gastroenterol Hepatol"},{"key":"3638_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.jhep.2020.02.027","volume":"73","author":"SA Harrison","year":"2020","unstructured":"Harrison SA, Wong VW-S, Okanoue T, Bzowej N, Vuppalanchi R, Younes Z, et al. Selonsertib for patients with bridging fibrosis or compensated cirrhosis due to NASH: Results from randomized phase III STELLAR trials. J Hepatol. 2020;73:26\u201339. https:\/\/doi.org\/10.1016\/j.jhep.2020.02.027.","journal-title":"J Hepatol"},{"key":"3638_CR9","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1172\/JCI24282","volume":"115","author":"R Bataller","year":"2005","unstructured":"Bataller R, Brenner DA. Liver fibrosis. J Clin Invest. 2005;115:209\u201318. https:\/\/doi.org\/10.1172\/JCI24282.","journal-title":"J Clin Invest"},{"key":"3638_CR10","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1021\/cbmi.5c00055","volume":"3","author":"X Hu","year":"2025","unstructured":"Hu X, Xu M, Xiao X, Wang N, Zhang Q, Ge J, et al. Molecular Imaging Probes for Early Detection and Staging of Liver Fibrosis. Chem Biomed Imaging. 2025;3:702\u201314. https:\/\/doi.org\/10.1021\/cbmi.5c00055.","journal-title":"Chem Biomed Imaging"},{"key":"3638_CR11","unstructured":"Diagnosis of non-alcoholic fatty liver disease\/non-alcoholic steatohepatitis: Non-invasive tests are enough - PubMed [Internet]. [cited 2026 Jan 10]. https:\/\/pubmed.ncbi.nlm.nih.gov\/29427494\/. Accessed 10 Jan 2026."},{"key":"3638_CR12","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1177\/10815589251342189","volume":"73","author":"M Helou","year":"2025","unstructured":"Helou M, Tatour M, Abu Baker F, Saadi T, Neeman Z, Hazzan R. Algorithmic screening of advanced liver fibrosis in a high-risk population and correlation with transient elastography results. J Investig Med. 2025;73:643\u20139. https:\/\/doi.org\/10.1177\/10815589251342189.","journal-title":"J Investig Med"},{"key":"3638_CR13","unstructured":"Circulating MicroRNAs: Diagnostic value as biomarkers in the detection of non-alcoholic fatty liver diseases and hepatocellular carcinoma - PubMed [Internet]. [cited 2026 Jan 10]. https:\/\/pubmed.ncbi.nlm.nih.gov\/37005546\/. Accessed 10 2026."},{"key":"3638_CR14","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ejrad.2018.09.019","volume":"108","author":"J Delahaye","year":"2018","unstructured":"Delahaye J, Bazeries P, Lannes A, Lebigot J, Cartier V, Oberti F, et al. Doppler ultrasonography devices, including elastography, allow for accurate diagnosis of severe liver fibrosis. Eur J Radiol. 2018;108:133\u20139. https:\/\/doi.org\/10.1016\/j.ejrad.2018.09.019.","journal-title":"Eur J Radiol"},{"key":"3638_CR15","unstructured":"The severity of steatosis influences liver stiffness measurement in patients with nonalcoholic fatty liver disease - PubMed [Internet]. [cited 2026 Jan 10]. https:\/\/pubmed.ncbi.nlm.nih.gov\/25991038\/. Accessed 10 Jan 2026."},{"key":"3638_CR16","doi-asserted-by":"publisher","first-page":"100176","DOI":"10.1016\/j.cmpbup.2024.100176","volume":"7","author":"D Lof\u00f9","year":"2025","unstructured":"Lof\u00f9 D, Sorino P, Colafiglio T, Bonfiglio C, Donghia R, Giannelli G, et al. Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects. Comput Methods Programs Biomed Update. 2025;7:100176. https:\/\/doi.org\/10.1016\/j.cmpbup.2024.100176.","journal-title":"Comput Methods Programs Biomed Update"},{"key":"3638_CR17","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1186\/s12876-024-03542-y","volume":"25","author":"H Badran","year":"2025","unstructured":"Badran H, Elsabaawy M, Magdy M, Omar H, Hendy O, Awaad E, et al. Platelet-derived growth factor receptor-\u03b2 as a non-invasive biomarker for liver fibrosis prediction in Egyptian diabetic patients with metabolic-associated fatty liver disease. BMC Gastroenterol. 2025;25:524. https:\/\/doi.org\/10.1186\/s12876-024-03542-y.","journal-title":"BMC Gastroenterol"},{"key":"3638_CR18","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1097\/MEG.0000000000002998","volume":"37","author":"S Duan","year":"2025","unstructured":"Duan S, Chen M, Chen J, Shao Y, Jin X, Wang C, et al. Application of insulin resistance score in type 2 diabetes mellitus complicated with fatty liver and liver fibrosis. Eur J Gastroenterol Hepatol. 2025;37:1155\u201365. https:\/\/doi.org\/10.1097\/MEG.0000000000002998.","journal-title":"Eur J Gastroenterol Hepatol"},{"key":"3638_CR19","first-page":"494","volume":"27","author":"Chinese Society of Hepatology, Chinese Medical Association","year":"2024","unstructured":"Chinese Society of Hepatology, Chinese Medical Association. Guidelines for the prevention and treatment of metabolic-associated fatty liver disease (2024 edition). J Practical Hepatol. 2024;27:494\u2013510.","journal-title":"J Practical Hepatol"},{"key":"3638_CR20","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1097\/HEP.0000000000000323","volume":"77","author":"ME Rinella","year":"2023","unstructured":"Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 2023;77:1797\u2013835. https:\/\/doi.org\/10.1097\/HEP.0000000000000323.","journal-title":"Hepatology"},{"key":"3638_CR21","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.jhep.2021.05.025","volume":"75","author":"European Association for the Study of the Liver","year":"2021","unstructured":"European Association for the Study of the Liver. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis\u2009\u2013\u20092021 update. J Hepatol. 2021;75:659\u201389. https:\/\/doi.org\/10.1016\/j.jhep.2021.05.025.","journal-title":"J Hepatol"},{"issue":"Suppl 1","key":"3638_CR22","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1111\/liv.14347","volume":"40","author":"L Castera","year":"2020","unstructured":"Castera L. Non-invasive tests for liver fibrosis in NAFLD: Creating pathways between primary healthcare and liver clinics. Liver Int. 2020;40(Suppl 1):77\u201381. https:\/\/doi.org\/10.1111\/liv.14347.","journal-title":"Liver Int"},{"key":"3638_CR23","doi-asserted-by":"publisher","first-page":"289","DOI":"10.12997\/jla.2025.14.3.289","volume":"14","author":"SG Kwak","year":"2025","unstructured":"Kwak SG. LASSO Regression Analysis: Applications in Dyslipidemia and Cardiovascular Disease Research. J Lipid Atheroscler. 2025;14:289\u201397. https:\/\/doi.org\/10.12997\/jla.2025.14.3.289.","journal-title":"J Lipid Atheroscler"},{"key":"3638_CR24","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1186\/s12879-025-10969-5","volume":"25","author":"L Liu","year":"2025","unstructured":"Liu L, Ma Q, Yu G, Ji X, He H. Association between the (neutrophil\u2009+\u2009monocyte)\/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning. BMC Infect Dis. 2025;25:579. https:\/\/doi.org\/10.1186\/s12879-025-10969-5.","journal-title":"BMC Infect Dis"},{"key":"3638_CR25","doi-asserted-by":"publisher","first-page":"1604832","DOI":"10.3389\/fcell.2025.1604832","volume":"13","author":"Z Huang","year":"2025","unstructured":"Huang Z, Gong D, Tang C, Wang J, Zhang C, Dang K, et al. A risk prediction model for neovascular glaucoma secondary to proliferative diabetic retinopathy based on Boruta feature selection and random forest. Front Cell Dev Biol. 2025;13:1604832. https:\/\/doi.org\/10.3389\/fcell.2025.1604832.","journal-title":"Front Cell Dev Biol"},{"key":"3638_CR26","doi-asserted-by":"publisher","first-page":"105889","DOI":"10.1016\/j.ijmedinf.2025.105889","volume":"199","author":"W Jin","year":"2025","unstructured":"Jin W, Xu L, Yue C, Hu L, Wang Y, Fu Y, et al. Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records. Int J Med Inf. 2025;199:105889. https:\/\/doi.org\/10.1016\/j.ijmedinf.2025.105889.","journal-title":"Int J Med Inf"},{"key":"3638_CR27","unstructured":"A simple risk scoring system for predicting the occurrence of aspiration pneumonia after gastric endoscopic submucosal dissection - PubMed [Internet]. [cited 2026 Jan 11]. https:\/\/pubmed.ncbi.nlm.nih.gov\/34673667\/. Accessed 11 Jan 2026."},{"key":"3638_CR28","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3390\/diagnostics15030307","volume":"15","author":"A Kotter","year":"2025","unstructured":"Kotter A, Abdelrahman S, Wan Y-KJ, Madaras-Kelly K, Morgan KL, Kan CFK, et al. Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score. Diagnostics (Basel). 2025;15:307. https:\/\/doi.org\/10.3390\/diagnostics15030307.","journal-title":"Diagnostics (Basel)"},{"key":"3638_CR29","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1186\/s12967-021-02990-4","volume":"19","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Yang D, Liu Z, Chen C, Ge M, Li X, et al. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation. J Transl Med. 2021;19:321. https:\/\/doi.org\/10.1186\/s12967-021-02990-4.","journal-title":"J Transl Med"},{"key":"3638_CR30","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1186\/s12933-025-02654-3","volume":"24","author":"Y Guo","year":"2025","unstructured":"Guo Y, Wang F, Ma S, Mao Z, Zhao S, Sui L, et al. Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning. Cardiovasc Diabetol. 2025;24:95. https:\/\/doi.org\/10.1186\/s12933-025-02654-3.","journal-title":"Cardiovasc Diabetol"},{"key":"3638_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-025-07528-0","author":"Z Jiang","year":"2025","unstructured":"Jiang Z, Spielvogel C, Haberl D, Yu J, Krisch M, Szakall S, et al. Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness. Eur J Nucl Med Mol Imaging. 2025. https:\/\/doi.org\/10.1007\/s00259-025-07528-0.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"3638_CR32","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s12072-024-10663-9","volume":"18","author":"SE Keating","year":"2024","unstructured":"Keating SE, Chawla Y, De A, George ES. Lifestyle intervention for metabolic dysfunction-associated fatty liver disease: a 24-h integrated behavior perspective. Hepatol Int. 2024;18:959\u201376. https:\/\/doi.org\/10.1007\/s12072-024-10663-9.","journal-title":"Hepatol Int"},{"key":"3638_CR33","doi-asserted-by":"publisher","first-page":"1081096","DOI":"10.3389\/fendo.2023.1081096","volume":"14","author":"X-N Chai","year":"2023","unstructured":"Chai X-N, Zhou B-Q, Ning N, Pan T, Xu F, He S-H, et al. Effects of lifestyle intervention on adults with metabolic associated fatty liver disease: A systematic review and meta-analysis. Front Endocrinol (Lausanne). 2023;14:1081096. https:\/\/doi.org\/10.3389\/fendo.2023.1081096.","journal-title":"Front Endocrinol (Lausanne)"},{"key":"3638_CR34","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/S1470-2045(22)00078-X","volume":"23","author":"DJH Tan","year":"2022","unstructured":"Tan DJH, Ng CH, Lin SY, Pan XH, Tay P, Lim WH, et al. Clinical characteristics, surveillance, treatment allocation, and outcomes of non-alcoholic fatty liver disease-related hepatocellular carcinoma: a systematic review and meta-analysis. Lancet Oncol. 2022;23:521\u201330. https:\/\/doi.org\/10.1016\/S1470-2045(22)00078-X.","journal-title":"Lancet Oncol"},{"key":"3638_CR35","doi-asserted-by":"publisher","first-page":"S47","DOI":"10.1016\/j.jhep.2014.12.012","volume":"62","author":"CD Byrne","year":"2015","unstructured":"Byrne CD, Targher G. NAFLD: a multisystem disease. J Hepatol. 2015;62:S47\u201364. https:\/\/doi.org\/10.1016\/j.jhep.2014.12.012.","journal-title":"J Hepatol"},{"key":"3638_CR36","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.jhep.2017.06.003","volume":"67","author":"J-G Fan","year":"2017","unstructured":"Fan J-G, Kim S-U, Wong VW-S. New trends on obesity and NAFLD in Asia. J Hepatol. 2017;67:862\u201373. https:\/\/doi.org\/10.1016\/j.jhep.2017.06.003.","journal-title":"J Hepatol"},{"key":"3638_CR37","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.3945\/ajcn.117.153148","volume":"105","author":"L Schwingshackl","year":"2017","unstructured":"Schwingshackl L, Schwedhelm C, Hoffmann G, Lampousi A-M, Kn\u00fcppel S, Iqbal K, et al. Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr. 2017;105:1462\u201373. https:\/\/doi.org\/10.3945\/ajcn.117.153148.","journal-title":"Am J Clin Nutr"},{"key":"3638_CR38","doi-asserted-by":"publisher","first-page":"154170","DOI":"10.1016\/j.metabol.2020.154170","volume":"111S","author":"A Mantovani","year":"2020","unstructured":"Mantovani A, Scorletti E, Mosca A, Alisi A, Byrne CD, Targher G. Complications, morbidity and mortality of nonalcoholic fatty liver disease. Metabolism. 2020;111S:154170. https:\/\/doi.org\/10.1016\/j.metabol.2020.154170.","journal-title":"Metabolism"},{"key":"3638_CR39","unstructured":"Metabolic puzzle: exploring liver fibrosis differences in Asian metabolic-associated fatty liver disease subtypes - PubMed [Internet]. [cited 2026 Jan 10]. https:\/\/pubmed.ncbi.nlm.nih.gov\/38313248\/. Accessed 10 Jan 2026."},{"key":"3638_CR40","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1002\/hep4.1990","volume":"6","author":"X Wang","year":"2022","unstructured":"Wang X, Li X, Jin R, Yang J, Huang R, Wei L, et al. Mid-upper arm circumference is associated with liver steatosis and fibrosis in patients with metabolic-associated fatty liver disease: A population based observational study. Hepatol Commun. 2022;6:2262\u201372. https:\/\/doi.org\/10.1002\/hep4.1990.","journal-title":"Hepatol Commun"},{"key":"3638_CR41","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.14218\/JCTH.2021.00371","volume":"10","author":"C Shao","year":"2022","unstructured":"Shao C, Ye J, Li X, Lin Y, Feng S, Liao B, et al. Discrepancies between Nonalcoholic and Metabolic-associated Fatty Liver Disease by Multiple Steatosis Assessment. J Clin Transl Hepatol. 2022;10:1013\u201326. https:\/\/doi.org\/10.14218\/JCTH.2021.00371.","journal-title":"J Clin Transl Hepatol"},{"key":"3638_CR42","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1111\/liv.14767","volume":"41","author":"A Rispo","year":"2021","unstructured":"Rispo A, Imperatore N, Guarino M, Tortora R, Alisi A, Cossiga V, et al. Metabolic-associated fatty liver disease (MAFLD) in coeliac disease. Liver Int. 2021;41:788\u201398. https:\/\/doi.org\/10.1111\/liv.14767.","journal-title":"Liver Int"},{"key":"3638_CR43","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1186\/s12876-025-03850-x","volume":"25","author":"L Huang","year":"2025","unstructured":"Huang L, Luo Y, Zhang L, Wu M, Hu L. Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021\u20132023. BMC Gastroenterol. 2025;25:255. https:\/\/doi.org\/10.1186\/s12876-025-03850-x.","journal-title":"BMC Gastroenterol"},{"key":"3638_CR44","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1111\/liv.15818","volume":"44","author":"R Fan","year":"2024","unstructured":"Fan R, Yu N, Li G, Arshad T, Liu W-Y, Wong GL-H, et al. Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis. Liver Int. 2024;44:749\u201359. https:\/\/doi.org\/10.1111\/liv.15818.","journal-title":"Liver Int"},{"key":"3638_CR45","doi-asserted-by":"publisher","first-page":"689","DOI":"10.3748\/wjg.v28.i6.689","volume":"28","author":"J-G Hong","year":"2022","unstructured":"Hong J-G, Yan L-J, Li X, Yao S-Y, Su P, Li H-C, et al. Comments on validation of conventional non-invasive fibrosis scoring systems in patients with metabolic associated fatty liver disease. World J Gastroenterol. 2022;28:689\u201392. https:\/\/doi.org\/10.3748\/wjg.v28.i6.689.","journal-title":"World J Gastroenterol"},{"key":"3638_CR46","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/17474124.2025.2450717","volume":"19","author":"R Righetti","year":"2025","unstructured":"Righetti R, Cinque F, Patel K, Sebastiani G. The role of noninvasive biomarkers for monitoring cell injury in advanced liver fibrosis. Expert Rev Gastroenterol Hepatol. 2025;19:65\u201380. https:\/\/doi.org\/10.1080\/17474124.2025.2450717.","journal-title":"Expert Rev Gastroenterol Hepatol"},{"key":"3638_CR47","doi-asserted-by":"publisher","first-page":"e2102","DOI":"10.1002\/hsr2.2102","volume":"7","author":"HNN Dang","year":"2024","unstructured":"Dang HNN, Luong TV, Tran TT, Hoang TA. The correlation between liver fibrosis and the 10-year estimated risk of cardiovascular disease in adults with metabolic-associated fatty liver disease: A cross-sectional study in Vietnam. Health Sci Rep. 2024;7:e2102. https:\/\/doi.org\/10.1002\/hsr2.2102.","journal-title":"Health Sci Rep"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03638-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03638-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03638-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T03:18:22Z","timestamp":1791256702000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03638-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,11]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3638"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03638-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-8976368\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,11]]},"assertion":[{"value":"26 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The ethical approval for this study (2025-IIT-KS-29) was granted by the Ethics Committee of Chongqing Hospital of Traditional Chinese Medicine in China on December 15, 2025. All procedures were conducted in accordance with the ethical standards of the institutional committees and the 1964 Helsinki Declaration and its later amendments. The Ethics Review Committee of Chongqing Hospital of Traditional Chinese Medicine has determined that this research can be exempted from obtaining informed consent.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethics Committee due to the retrospective nature of the study and the use of deidentified\/anonymized clinical data.","order":2,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"392"}}