{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:49:29Z","timestamp":1792198169012,"version":"4.4.0"},"reference-count":96,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005851","name":"Shahid Beheshti University of Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005851","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1016\/j.cmpb.2023.107932","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T03:56:57Z","timestamp":1700625417000},"page":"107932","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":32,"special_numbering":"C","title":["Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005\u20132023)"],"prefix":"10.1016","volume":"244","author":[{"given":"H.","family":"Zamanian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Shalbaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.R.","family":"Zali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.R.","family":"Khalaj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Dehghan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Tabesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Hatami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Alizadehsani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"Suppl 1","key":"10.1016\/j.cmpb.2023.107932_bib0001","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/j.1440-1746.2010.06547.x","article-title":"Nonalcoholic fatty liver disease and nonalcoholic steatohepatitis in Japan","volume":"26","author":"Okanoue","year":"2011","journal-title":"J. Gastroenterol. Hepatol."},{"key":"10.1016\/j.cmpb.2023.107932_bib0002","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1002\/hep.24097","article-title":"Genderspecific differences in adipose distribution and adipocytokines influence adolescent nonalcoholic fatty liver disease","volume":"53","author":"Avonrinde","year":"2011","journal-title":"Hepatology"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0003","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1016\/0016-5085(94)90235-6","article-title":"Nonalcoholic steatohepatitis: an expanded clinical entity","volume":"107","author":"Bacon","year":"1994","journal-title":"Gastroenterology"},{"key":"10.1016\/j.cmpb.2023.107932_bib0004","doi-asserted-by":"crossref","DOI":"10.1155\/2011\/831536","article-title":"Mechanisms and implications of age-related changes in the liver: nonalcoholic fatty liver disease in the elderly","volume":"2011","author":"Gan","year":"2011","journal-title":"Curr. Gerontol. Geriatr. Res."},{"issue":"16","key":"10.1016\/j.cmpb.2023.107932_bib0005","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1056\/NEJMra035021","article-title":"Management of cirrhosis and ascites","volume":"350","author":"Gin\u00e8s","year":"2004","journal-title":"New Engl. J. Med."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-230X-6-33","article-title":"The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population","volume":"6","author":"Bedogni","year":"2006","journal-title":"BMC Gastroenterol"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1476-5926-4-10","article-title":"The diagnostic value of biomarkers (SteatoTest) for the prediction of liver steatosis","volume":"4","author":"Poynard","year":"2005","journal-title":"Comp Hepatol"},{"issue":"7","key":"10.1016\/j.cmpb.2023.107932_bib0008","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.dld.2009.08.002","article-title":"Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease","volume":"42","author":"Lee","year":"2010","journal-title":"Digest. Liver Disease"},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0009","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1002\/hep.21178","article-title":"Development of a simple noninvasive index to predict significant fibrosis in patients with HIV\/HCV coinfection","volume":"43","author":"Sterling","year":"2006","journal-title":"Hepatology"},{"issue":"9262","key":"10.1016\/j.cmpb.2023.107932_bib0010","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1016\/S0140-6736(00)04258-6","article-title":"Biochemical markers of liver fibrosis in patients with hepatitis C virus infection: a prospective study","volume":"357","author":"Imbert-Bismut","year":"2001","journal-title":"Lancet"},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0011","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1016\/S0016-5085(00)70364-7","article-title":"Liver fibrosis in overweight patients","volume":"118","author":"Ratziu","year":"2000","journal-title":"Gastroenterology"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0012","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1002\/hep.21496","article-title":"The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD","volume":"45","author":"Angulo","year":"2007","journal-title":"Hepatology"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0013","doi-asserted-by":"crossref","first-page":"790","DOI":"10.3390\/cancers13040790","article-title":"Performance of ultrasound techniques and the potential of artificial intelligence in the evaluation of hepatocellular carcinoma and non-alcoholic fatty liver disease","volume":"13","author":"Lupsor-Platon","year":"2021","journal-title":"Cancers"},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0014","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1053\/j.gastro.2019.01.042","article-title":"Accuracy of FibroScan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease","volume":"156","author":"Eddowes","year":"2019","journal-title":"Gastroenterology"},{"issue":"2","key":"10.1016\/j.cmpb.2023.107932_bib0015","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1148\/radiol.2021204288","article-title":"Quantification of liver fat content with CT and MRI: state of the art","volume":"301","author":"Starekova","year":"2021","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0016","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1002\/jmri.22580","article-title":"Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy","volume":"34","author":"Reeder","year":"2011","journal-title":"J. Magnet. Reson. Imag."},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0017","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1002\/hep.20701","article-title":"Design and validation of a histological scoring system for nonalcoholic fatty liver disease","volume":"41","author":"Kleiner","year":"2005","journal-title":"Hepatology"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0018","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1002\/hep.31869","article-title":"Application of artificial intelligence for diagnosis and risk stratification in NAFLD and NASH: the state of the art","volume":"74","author":"Dinani","year":"2021","journal-title":"Hepatology"},{"key":"10.1016\/j.cmpb.2023.107932_bib0019","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.neucom.2021.08.138","article-title":"Machine learning based liver disease diagnosis: a systematic review","volume":"468","author":"Khan","year":"2022","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0020","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s11154-021-09681-x","article-title":"Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease (NAFLD): a systematic review","volume":"23","author":"Li","year":"2022","journal-title":"Rev. Endocr. Metabol. Disord."},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0021","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.3233\/IDA-216379","article-title":"Diagnosis of liver disease by computer-assisted imaging techniques: a literature review","volume":"26","author":"Kalejahi","year":"2022","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.cmpb.2023.107932_bib0022","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10620-005-1267-z","article-title":"Nonalcoholic fatty liver disease: a clinical review","volume":"50","author":"Sass","year":"2005","journal-title":"Dig. Dis. Sci."},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0023","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1381\/0960892053576596","article-title":"Non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma","volume":"15","author":"Cuadrado","year":"2005","journal-title":"Obes Surg"},{"issue":"7","key":"10.1016\/j.cmpb.2023.107932_bib0024","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1097\/01.mcg.0000170742.59134.60","article-title":"Liver biopsy: evolving role in the new millennium","volume":"39","author":"Sheela","year":"2005","journal-title":"J. Clin. Gastroenterol."},{"key":"10.1016\/j.cmpb.2023.107932_bib0025","article-title":"PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"bmj"},{"issue":"7","key":"10.1016\/j.cmpb.2023.107932_bib0026","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1148\/rg.2016160042","article-title":"Elastography in chronic liver disease: modalities, techniques, limitations, and future directions","volume":"36","author":"Srinivasa Babu","year":"2016","journal-title":"Radiographics"},{"key":"10.1016\/j.cmpb.2023.107932_bib0027","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.cmpb.2018.10.006","article-title":"Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features","volume":"166","author":"Acharya","year":"2018","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0028","first-page":"145","article-title":"Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD","volume":"38","author":"Bharath","year":"2018","journal-title":"Biocyber. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2023.107932_bib0029","series-title":"Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","first-page":"1","article-title":"Objective liver fibrosis estimation from shear wave elastography","author":"Brattain","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107932_bib0030","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.compbiomed.2017.07.012","article-title":"Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B","volume":"89","author":"Chen","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2023.107932_bib0031","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.protcy.2012.09.084","article-title":"Classifier approaches for liver steatosis using ultrasound images","volume":"5","author":"Andrade","year":"2012","journal-title":"Proc. Technol."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0032","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0262291","article-title":"Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease","volume":"17","author":"Destrempes","year":"2022","journal-title":"PLoS ONE"},{"issue":"7","key":"10.1016\/j.cmpb.2023.107932_bib0033","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1002\/jhbp.972","article-title":"Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD","volume":"28","author":"Feng","year":"2021","journal-title":"J. Hepatobil. Pancreat Sci."},{"key":"10.1016\/j.cmpb.2023.107932_bib0034","unstructured":"Sukmaningtyas H. Quantification of hepatic fat level using gray level co-occurrence matrix (GLCM) and extreme learning machine (ELM) methods in ultrasonography image (USG), 2020."},{"issue":"10","key":"10.1016\/j.cmpb.2023.107932_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-017-0797-1","article-title":"Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization","volume":"41","author":"Kuppili","year":"2017","journal-title":"J. Med. Syst."},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0036","doi-asserted-by":"crossref","first-page":"184","DOI":"10.11152\/mu.2013.2066.153.dmm1vg2","article-title":"Computer aided diagnosis method for steatosis rating in ultrasound images using random forests","volume":"15","author":"Mihailescu","year":"2013","journal-title":"Med. Ultrason"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0037","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1002\/jum.15154","article-title":"Ultrasound fatty liver indicator: a simple tool for differentiating steatosis from nonalcoholic steatohepatitis: validity in the average obese population","volume":"39","author":"Nelson","year":"2020","journal-title":"J. Ultras. Med."},{"issue":"2","key":"10.1016\/j.cmpb.2023.107932_bib0038","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1177\/0161734616649153","article-title":"Staging of fatty liver diseases based on hierarchical classification and feature fusion for back-scan\u2013converted ultrasound images","volume":"39","author":"Owjimehr","year":"2017","journal-title":"Ultrason Imaging"},{"issue":"5","key":"10.1016\/j.cmpb.2023.107932_bib0039","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.irbm.2018.09.006","article-title":"Automated classification of fatty and normal liver ultrasound images based on mutual information feature selection","volume":"39","author":"Sharma","year":"2018","journal-title":"Irbm"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0040","doi-asserted-by":"crossref","first-page":"521","DOI":"10.3390\/app12010521","article-title":"Automatic classification of fatty liver disease based on supervised learning and genetic algorithm","volume":"12","author":"Gaber","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.cmpb.2023.107932_bib0041","series-title":"Proceedings of the IEEE 20th international conference on e-health networking, applications and services (Healthcom)","first-page":"1","article-title":"A novel computer-aided diagnosis framework using deep learning for classification of fatty liver disease in ultrasound imaging","author":"Reddy","year":"2018"},{"issue":"10","key":"10.1016\/j.cmpb.2023.107932_bib0042","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1016\/j.ultrasmedbio.2020.05.016","article-title":"Image processing pipeline for liver fibrosis classification using ultrasound shear wave elastography","volume":"46","author":"Brattain","year":"2020","journal-title":"Ultrasound Med. Biol."},{"issue":"12","key":"10.1016\/j.cmpb.2023.107932_bib0043","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1007\/s11548-018-1843-2","article-title":"Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images","volume":"13","author":"Byra","year":"2018","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0044","first-page":"188","article-title":"Automatic recognition of non-alcoholic fatty liver by using transfer deep learning networks from ultrasound images","volume":"25","author":"Zamanian","year":"2020","journal-title":"GOVARESH"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0045","doi-asserted-by":"crossref","first-page":"73","DOI":"10.31661\/jbpe.v0i0.2009-1180","article-title":"Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images","volume":"11","author":"Zamanian","year":"2021","journal-title":"J. Biomed. Phys. Eng."},{"key":"10.1016\/j.cmpb.2023.107932_bib0046","series-title":"Proceedings of the IEEE International Ultrasonics Symposium (IUS)","first-page":"1","article-title":"Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method","author":"Byra","year":"2020"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0047","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1002\/jum.15693","article-title":"Liver fat assessment in multiview sonography using transfer learning with convolutional neural networks","volume":"41","author":"Byra","year":"2022","journal-title":"J. Ultras. Med."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0048","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1002\/jum.15070","article-title":"Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease","volume":"39","author":"Cao","year":"2020","journal-title":"J. Ultras. Med."},{"issue":"9","key":"10.1016\/j.cmpb.2023.107932_bib0049","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1007\/s11548-021-02414-0","article-title":"Liver disease classification from ultrasound using multi-scale CNN","volume":"16","author":"Che","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"9","key":"10.1016\/j.cmpb.2023.107932_bib0050","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1097\/JCMA.0000000000000585","article-title":"Deep learning for abdominal ultrasound: a computer-aided diagnostic system for the severity of fatty liver","volume":"84","author":"Chou","year":"2021","journal-title":"J. Chinese Med. Assoc."},{"key":"10.1016\/j.cmpb.2023.107932_bib0051","series-title":"Proceedings of the IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","first-page":"1","article-title":"Classification of nonalcoholic fatty liver texture using convolution neural networks","author":"Reddy","year":"2018"},{"issue":"2","key":"10.1016\/j.cmpb.2023.107932_bib0052","first-page":"135","article-title":"Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images","volume":"23","author":"Constantinescu","year":"2021","journal-title":"Med. Ultrason."},{"key":"10.1016\/j.cmpb.2023.107932_bib0053","series-title":"Proceedings of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","first-page":"1","article-title":"Classification of diffuse liver diseases based on ultrasound images with multimodal features","author":"Dandan","year":"2019"},{"issue":"2","key":"10.1016\/j.cmpb.2023.107932_bib0054","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1148\/radiol.2020191160","article-title":"Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks","volume":"295","author":"Han","year":"2020","journal-title":"Radiology"},{"issue":"11","key":"10.1016\/j.cmpb.2023.107932_bib0055","doi-asserted-by":"crossref","first-page":"e30066","DOI":"10.2196\/30066","article-title":"Deep learning techniques for fatty liver using multi-view ultrasound images scanned by different scanners: development and validation study","volume":"9","author":"Kim","year":"2021","journal-title":"JMIR Med Inform"},{"issue":"22","key":"10.1016\/j.cmpb.2023.107932_bib0056","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.3748\/wjg.v28.i22.2494","article-title":"Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning","volume":"28","author":"Li","year":"2022","journal-title":"World J. Gastroenterol."},{"issue":"16","key":"10.1016\/j.cmpb.2023.107932_bib0057","doi-asserted-by":"crossref","first-page":"5304","DOI":"10.3390\/s21165304","article-title":"Cascaded deep learning neural network for automated liver steatosis diagnosis using ultrasound images","volume":"21","author":"Rhyou","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.cmpb.2023.107932_bib0058","series-title":"Proceedings of the IEEE International Ultrasonics Symposium (IUS)","first-page":"1","article-title":"Learning steatosis staging with two-dimensional Convolutional Neural Networks: comparison of accuracy of clinical B-mode with a co-registered spectrogram representation of RF Data","author":"Sanabria","year":"2020"},{"key":"10.1016\/j.cmpb.2023.107932_bib0059","doi-asserted-by":"crossref","first-page":"S33","DOI":"10.1016\/j.ultrasmedbio.2019.07.516","article-title":"Assessing hepatic steatosis on ultrasound imaging using deep learning","volume":"45","author":"Tang","year":"2019","journal-title":"Ultrasound Med Biol"},{"issue":"5","key":"10.1016\/j.cmpb.2023.107932_bib0060","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1007\/s00330-019-06595-w","article-title":"Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis","volume":"30","author":"Xue","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.cmpb.2023.107932_bib0061","series-title":"Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis","first-page":"354","article-title":"Fatty liver characterization and classification by ultrasound","author":"Ribeiro","year":"2009"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0062","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/JBHI.2013.2284785","article-title":"An ultrasound-based computer-aided diagnosis tool for steatosis detection","volume":"18","author":"Ribeiro","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.cmpb.2023.107932_bib0063","series-title":"Proceedings of the Explainable AI in Healthcare and Medicine","first-page":"107","article-title":"A deep learning approach for classifying nonalcoholic steatohepatitis patients from nonalcoholic fatty liver disease patients using electronic medical records","author":"Suresha","year":"2021"},{"key":"10.1016\/j.cmpb.2023.107932_bib0064","series-title":"Proceedings of the AMIA Annual Symposium Proceedings","first-page":"430","article-title":"Application of machine learning methods to predict non-alcoholic steatohepatitis (NASH) in non-alcoholic fatty liver (NAFL) patients","volume":"2018","author":"Fialoke","year":"2018"},{"issue":"2","key":"10.1016\/j.cmpb.2023.107932_bib0065","first-page":"31","article-title":"Machine learning technology for evaluation of liver fibrosis, inflammation activity and steatosis (LIVERFAStTM)","volume":"12","author":"Aravind","year":"2020","journal-title":"J. Intell. Learn. Syst. Appl."},{"issue":"11","key":"10.1016\/j.cmpb.2023.107932_bib0066","doi-asserted-by":"crossref","DOI":"10.5812\/ircmj.32858","article-title":"Prediction and diagnosis of non-alcoholic fatty liver disease (NAFLD) and identification of its associated factors using the classification tree method","volume":"18","author":"Birjandi","year":"2016","journal-title":"Iran Red Crescent Med J"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0067","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0214436","article-title":"Non-invasive assessment of NAFLD as systemic disease\u2014A machine learning perspective","volume":"14","author":"Canbay","year":"2019","journal-title":"PLoS ONE"},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0068","doi-asserted-by":"crossref","first-page":"763","DOI":"10.3390\/e23060763","article-title":"Assessment of classification models and relevant features on nonalcoholic steatohepatitis using random forest","volume":"23","author":"Garc\u00eda-Carretero","year":"2021","journal-title":"Entropy"},{"key":"10.1016\/j.cmpb.2023.107932_bib0069","series-title":"Proceedings of the International Conference on Data Science (ICDATA)","first-page":"118","article-title":"Application of machine learning methods to predict non-alcohol fatty liver disease in Taiwanese high-tech industry workers","author":"Cheng","year":"2017"},{"key":"10.1016\/j.cmpb.2023.107932_bib0070","series-title":"Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"5382","article-title":"Assisting the non-invasive diagnosis of liver fibrosis stages using machine learning methods","author":"Emu","year":"2020"},{"issue":"7","key":"10.1016\/j.cmpb.2023.107932_bib0071","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1002\/jhbp.972","article-title":"Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD","volume":"28","author":"Feng","year":"2021","journal-title":"J Hepatobiliary Pancreat Sci"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0072","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1002\/jgh3.12716","article-title":"Machine learning to predict progression of non-alcoholic fatty liver to non-alcoholic steatohepatitis or fibrosis","volume":"6","author":"Ghandian","year":"2022","journal-title":"JGH Open"},{"issue":"21","key":"10.1016\/j.cmpb.2023.107932_bib0073","doi-asserted-by":"crossref","DOI":"10.21037\/atm-20-2951","article-title":"Development of a simple nonalcoholic fatty liver disease scoring system indicative of metabolic risks and insulin resistance","volume":"8","author":"Jeong","year":"2020","journal-title":"Ann Transl Med"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0074","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/S2468-1253(19)30383-8","article-title":"FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study","volume":"5","author":"Newsome","year":"2020","journal-title":"lancet Gastroenterol. Hepatol."},{"issue":"10","key":"10.1016\/j.cmpb.2023.107932_bib0075","article-title":"Nonalcoholic fatty liver disease in a cluster of Iranian population: thyroid status and metabolic risk factors","volume":"16","author":"Eshraghian","year":"2013","journal-title":"Arch. Iran. Med"},{"issue":"5","key":"10.1016\/j.cmpb.2023.107932_bib0076","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1111\/hepr.13628","article-title":"Artificial intelligence\/neural network system for the screening of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis","volume":"51","author":"Okanoue","year":"2021","journal-title":"Hepatol. Res."},{"issue":"10","key":"10.1016\/j.cmpb.2023.107932_bib0077","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1111\/hepr.13681","article-title":"Novel artificial intelligent\/neural network system for staging of nonalcoholic steatohepatitis","volume":"51","author":"Okanoue","year":"2021","journal-title":"Hepatol. Res."},{"key":"10.1016\/j.cmpb.2023.107932_bib0078","doi-asserted-by":"crossref","DOI":"10.1016\/j.metabol.2019.154005","article-title":"Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: a proof of concept study","volume":"101","author":"Perakakis","year":"2019","journal-title":"Metabolism"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0079","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-20166-x","article-title":"A systematic machine learning based approach for the diagnosis of non-alcoholic fatty liver disease risk and progression","volume":"8","author":"Perveen","year":"2018","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.cmpb.2023.107932_bib0080","article-title":"Construction of computer model for diagnostics and monitoring of non-alcoholic fatty liver disease","volume":"2","author":"\u0421\u0430\u0434\u044b\u043a\u043e\u0432\u0430","year":"2021","journal-title":"\u0423\u0417\u0411\u0415\u041a\u0421\u041a\u0418\u0419 \u041c\u0415\u0414\u0418\u0426\u0418\u041d\u0421\u041a\u0418\u0419 \u0416\u0423\u0420\u041d\u0410\u041b"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107932_bib0081","doi-asserted-by":"crossref","first-page":"e62439","DOI":"10.1371\/journal.pone.0062439","article-title":"Novel algorithm for non-invasive assessment of fibrosis in NAFLD","volume":"8","author":"Sowa","year":"2013","journal-title":"PLoS ONE"},{"key":"10.1016\/j.cmpb.2023.107932_bib0082","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2018.12.032","article-title":"Prediction of fatty liver disease using machine learning algorithms","volume":"170","author":"Wu","year":"2019","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"5","key":"10.1016\/j.cmpb.2023.107932_bib0083","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.dld.2007.10.019","article-title":"Noninvasive assessment of liver fibrosis by measurement of stiffness in patients with nonalcoholic fatty liver disease (NAFLD)","volume":"40","author":"Yoneda","year":"2008","journal-title":"Digest. Liver Disease"},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0084","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.3390\/diagnostics13061168","article-title":"Prediction of fatty liver disease in a chinese population using machine-learning algorithms","volume":"13","author":"Weng","year":"2023","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0085","doi-asserted-by":"crossref","first-page":"4942","DOI":"10.1038\/s41598-023-32129-y","article-title":"Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices","volume":"13","author":"Razmpour","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2023.107932_bib0086","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2022.846118","article-title":"A machine learning based framework to identify and classify non-alcoholic fatty liver disease in a large-scale population","volume":"10","author":"Ji","year":"2022","journal-title":"Front. Public Health"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107932_bib0087","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-99400-y","article-title":"Development and validation of a neural network for NAFLD diagnosis","volume":"11","author":"Sorino","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2023.107932_bib0088","first-page":"1","article-title":"Estimation of non-alcoholic steatohepatitis (NASH) disease using clinical information based on the optimal combination of intelligent algorithms for feature selection and classification","author":"Zamanian","year":"2023","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"issue":"64","key":"10.1016\/j.cmpb.2023.107932_bib0089","first-page":"32","article-title":"Intelegence diagnostic system of liver fibrosis stages","volume":"2","author":"Sineglazov","year":"2020","journal-title":"Electron. Control Syst."},{"key":"10.1016\/j.cmpb.2023.107932_bib0090","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/4304376","article-title":"Application of machine learning techniques for clinical predictive modeling: a cross-sectional study on nonalcoholic fatty liver disease in China","volume":"2018","author":"Ma","year":"2018","journal-title":"Biomed. Res. Int."},{"issue":"3","key":"10.1016\/j.cmpb.2023.107932_bib0091","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"10.1016\/j.cmpb.2023.107932_bib0092","article-title":"Lightweight deep learning: an overview","author":"Wang","year":"2022","journal-title":"IEEE Cons. Electron. Magaz."},{"key":"10.1016\/j.cmpb.2023.107932_bib0093","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107441","article-title":"Application of uncertainty quantification to artificial intelligence in healthcare: a review of last decade (2013\u20132023)","author":"Seoni","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2023.107932_bib0094","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107161","article-title":"Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011\u20132022)","author":"Loh","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"issue":"6","key":"10.1016\/j.cmpb.2023.107932_bib0095","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.3390\/app13064006","article-title":"Effective class-imbalance learning based on SMOTE and convolutional neural networks","volume":"13","author":"Joloudari","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.cmpb.2023.107932_bib0096","series-title":"Proceedings of the IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)","first-page":"981","article-title":"Deep learning-based NAS Score and fibrosis stage prediction from CT and pathology data","author":"Jana","year":"2020"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723005989?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723005989?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:20:33Z","timestamp":1759206033000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260723005989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":96,"alternative-id":["S0169260723005989"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107932","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005\u20132023)","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107932","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107932"}}