{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:50:40Z","timestamp":1787673040998,"version":"build-2736575974"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MH028"],"award-info":[{"award-number":["ZR2021MH028"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Medical Science and Technology Development Plan of Shandong Province","award":["202003031362"],"award-info":[{"award-number":["202003031362"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90\u201393%) and 94% (95% CI: 93\u201396%), PLR and NLR were 12.67 (95% CI: 7.65\u201320.98) and 0.09 (95% CI: 0.06\u20130.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning\/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>AI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-01172-6","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T11:02:22Z","timestamp":1702292542000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"23","author":[{"given":"Qi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yadi","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xunjun","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"issue":"4","key":"1172_CR1","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1097\/HEP.0000000000000004","volume":"77","author":"ZM Younossi","year":"2023","unstructured":"Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty Liver Disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology (Baltimore MD). 2023;77(4):1335\u201347.","journal-title":"Hepatology (Baltimore MD)"},{"issue":"7","key":"1172_CR2","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1038\/s41591-018-0104-9","volume":"24","author":"SL Friedman","year":"2018","unstructured":"Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24(7):908\u201322.","journal-title":"Nat Med"},{"issue":"1","key":"1172_CR3","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1111\/jgh.13857","volume":"33","author":"VW Wong","year":"2018","unstructured":"Wong VW, Chan WK, Chitturi S, et al. Asia-Pacific Working Party on Non-alcoholic Fatty Liver Disease guidelines 2017-Part 1: definition, risk factors and assessment. J Gastroenterol Hepatol. 2018;33(1):70\u201385.","journal-title":"J Gastroenterol Hepatol"},{"issue":"8","key":"1172_CR4","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1056\/NEJMra1610570","volume":"377","author":"EB Tapper","year":"2017","unstructured":"Tapper EB, Lok AS. Use of Liver Imaging and Biopsy in Clinical Practice. N Engl J Med. 2017;377(8):756\u201368.","journal-title":"N Engl J Med"},{"issue":"10","key":"1172_CR5","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1111\/j.1572-0241.2002.06038.x","volume":"97","author":"A Regev","year":"2002","unstructured":"Regev A, Berho M, Jeffers LJ, et al. Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV Infection. Am J Gastroenterol. 2002;97(10):2614\u20138.","journal-title":"Am J Gastroenterol"},{"issue":"1","key":"1172_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jhep.2008.10.014","volume":"50","author":"P Bedossa","year":"2009","unstructured":"Bedossa P, Carrat F. Liver biopsy: the best, not the gold standard. J Hepatol. 2009;50(1):1\u20133.","journal-title":"J Hepatol"},{"issue":"2","key":"1172_CR7","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/S0168-8278(86)80075-7","volume":"2","author":"F Piccinino","year":"1986","unstructured":"Piccinino F, Sagnelli E, Pasquale G, Giusti G. Complications following percutaneous liver biopsy. A multicentre retrospective study on 68,276 biopsies. J Hepatol. 1986;2(2):165\u201373.","journal-title":"J Hepatol"},{"issue":"17","key":"1172_CR8","doi-asserted-by":"publisher","first-page":"e6585","DOI":"10.1097\/MD.0000000000006585","volume":"96","author":"P Phisalprapa","year":"2017","unstructured":"Phisalprapa P, Supakankunti S, Charatcharoenwitthaya P, et al. Cost-effectiveness analysis of ultrasonography screening for nonalcoholic fatty Liver Disease in metabolic syndrome patients. Medicine. 2017;96(17):e6585.","journal-title":"Medicine"},{"issue":"3","key":"1172_CR9","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1002\/hep.29596","volume":"67","author":"MS Middleton","year":"2018","unstructured":"Middleton MS, Van Natta ML, Heba ER, et al. Diagnostic accuracy of magnetic resonance imaging hepatic proton density fat fraction in pediatric nonalcoholic fatty Liver Disease. Hepatology (Baltimore MD). 2018;67(3):858\u201372.","journal-title":"Hepatology (Baltimore MD)"},{"issue":"3","key":"1172_CR10","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1148\/radiol.2020201434","volume":"297","author":"DJ Mollura","year":"2020","unstructured":"Mollura DJ, Culp MP, Pollack E, et al. Artificial Intelligence in Low- and Middle-Income countries: Innovating Global Health Radiology. Radiology. 2020;297(3):513\u201320.","journal-title":"Radiology"},{"key":"1172_CR11","doi-asserted-by":"crossref","unstructured":"Zhang Y, Weng Y, Lund J. Applications of explainable Artificial Intelligence in diagnosis and Surgery. Diagnostics (Basel Switzerland) 2022; 12(2).","DOI":"10.3390\/diagnostics12020237"},{"issue":"2","key":"1172_CR12","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, 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(2):342\u201350.","journal-title":"Radiology"},{"key":"1172_CR13","first-page":"n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical Research ed). 2021;372:n71.","journal-title":"BMJ (Clinical Research ed)"},{"issue":"10","key":"1172_CR14","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","volume":"27","author":"V Sounderajah","year":"2021","unstructured":"Sounderajah V, Ashrafian H, Rose S, et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med. 2021;27(10):1663\u20135.","journal-title":"Nat Med"},{"issue":"8","key":"1172_CR15","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529\u201336.","journal-title":"Ann Intern Med"},{"issue":"11","key":"1172_CR16","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.7326\/M21-2234","volume":"174","author":"B Yang","year":"2021","unstructured":"Yang B, Mallett S, Takwoingi Y, et al. QUADAS-C: a Tool for assessing risk of Bias in Comparative Diagnostic Accuracy studies. Ann Intern Med. 2021;174(11):1592\u20139.","journal-title":"Ann Intern Med"},{"issue":"4857","key":"1172_CR17","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1126\/science.3287615","volume":"240","author":"JA Swets","year":"1988","unstructured":"Swets JA. Measuring the accuracy of diagnostic systems. Sci (New York NY). 1988;240(4857):1285\u201393.","journal-title":"Sci (New York NY)"},{"key":"1172_CR18","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.cmpb.2016.03.016","volume":"130","author":"L Saba","year":"2016","unstructured":"Saba L, Dey N, Ashour AS, et al. Automated stratification of Liver Disease in ultrasound: an online accurate feature classification paradigm. Comput Methods Programs Biomed. 2016;130:118\u201334.","journal-title":"Comput Methods Programs Biomed"},{"issue":"5","key":"1172_CR19","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1007\/s10916-011-9803-1","volume":"36","author":"F Minhas","year":"2012","unstructured":"Minhas F, Sabih D, Hussain M. Automated classification of liver disorders using ultrasound images. J Med Syst. 2012;36(5):3163\u201372.","journal-title":"J Med Syst"},{"key":"1172_CR20","doi-asserted-by":"crossref","unstructured":"Li G, Luo Y, Deng W, Xu X, Liu A, Song E. Computer aided diagnosis of fatty liver ultrasonic images based on support vector machine. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference 2008; 2008: 4768-71.","DOI":"10.1109\/IEMBS.2008.4650279"},{"issue":"10","key":"1172_CR21","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/s10916-017-0797-1","volume":"41","author":"V Kuppili","year":"2017","unstructured":"Kuppili V, Biswas M, Sreekumar A, et al. Extreme Learning Machine Framework for risk stratification of fatty Liver Disease using Ultrasound tissue characterization. J Med Syst. 2017;41(10):152.","journal-title":"J Med Syst"},{"issue":"5","key":"1172_CR22","first-page":"297","volume":"24","author":"M H\u00e1jek","year":"2011","unstructured":"H\u00e1jek M, Dezortov\u00e1 M, Wagnerov\u00e1 D, et al. MR spectroscopy as a tool for in vivo determination of steatosis in liver transplant recipients. Magma (New York NY). 2011;24(5):297\u2013304.","journal-title":"Magma (New York NY)"},{"issue":"1","key":"1172_CR23","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1002\/jum.15693","volume":"41","author":"M Byra","year":"2022","unstructured":"Byra M, Han A, Boehringer AS, et al. Liver Fat Assessment in Multiview Sonography using transfer learning with convolutional neural networks. J Ultrasound Medicine: Official J Am Inst Ultrasound Med. 2022;41(1):175\u201384.","journal-title":"J Ultrasound Medicine: Official J Am Inst Ultrasound Med"},{"key":"1172_CR24","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","volume":"155","author":"M Biswas","year":"2018","unstructured":"Biswas M, Kuppili V, Edla DR, et al. Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed. 2018;155:165\u201377.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"1172_CR25","first-page":"135","volume":"23","author":"EC Constantinescu","year":"2021","unstructured":"Constantinescu EC, Udri\u0219toiu AL, Udri\u0219toiu \u0218C, et al. Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images. Med Ultrasonography. 2021;23(2):135\u20139.","journal-title":"Med Ultrasonography"},{"issue":"1","key":"1172_CR26","doi-asserted-by":"publisher","first-page":"e0262291","DOI":"10.1371\/journal.pone.0262291","volume":"17","author":"F Destrempes","year":"2022","unstructured":"Destrempes F, Gesnik M, Chayer B, et al. Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic Liver Disease. PLoS ONE. 2022;17(1):e0262291.","journal-title":"PLoS ONE"},{"issue":"1","key":"1172_CR27","doi-asserted-by":"publisher","first-page":"21","DOI":"10.4103\/2228-7477.150387","volume":"5","author":"M Owjimehr","year":"2015","unstructured":"Owjimehr M, Danyali H, Helfroush MS. An improved method for Liver Diseases detection by ultrasound image analysis. J Med Signals Sens. 2015;5(1):21\u20139.","journal-title":"J Med Signals Sens"},{"key":"1172_CR28","doi-asserted-by":"crossref","unstructured":"Sharma V, Juglan KC. Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection. IRBM 2018; 39(5): 313\u2009\u2013\u200923.","DOI":"10.1016\/j.irbm.2018.09.006"},{"key":"1172_CR29","doi-asserted-by":"crossref","unstructured":"Ribeiro R, Tato Marinho R, Sanches JM. Global and local detection of liver steatosis from ultrasound. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference 2012; 2012: 6547-50.","DOI":"10.1109\/EMBC.2012.6347494"},{"issue":"4","key":"1172_CR30","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/JBHI.2013.2284785","volume":"18","author":"RT Ribeiro","year":"2014","unstructured":"Ribeiro RT, Marinho RT, Sanches JM. An ultrasound-based computer-aided diagnosis tool for steatosis detection. IEEE J Biomedical Health Inf. 2014;18(4):1397\u2013403.","journal-title":"IEEE J Biomedical Health Inf"},{"issue":"7","key":"1172_CR31","doi-asserted-by":"publisher","first-page":"4255","DOI":"10.1118\/1.4725759","volume":"39","author":"UR Acharya","year":"2012","unstructured":"Acharya UR, Sree SV, Ribeiro R, et al. Data mining framework for fatty Liver Disease classification in ultrasound: a hybrid feature extraction paradigm. Med Phys. 2012;39(7):4255\u201364.","journal-title":"Med Phys"},{"issue":"6","key":"1172_CR32","doi-asserted-by":"publisher","first-page":"672","DOI":"10.3748\/wjg.v25.i6.672","volume":"25","author":"LQ Zhou","year":"2019","unstructured":"Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol. 2019;25(6):672\u201382.","journal-title":"World J Gastroenterol"},{"issue":"9","key":"1172_CR33","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1111\/liv.14555","volume":"40","author":"J Wei","year":"2020","unstructured":"Wei J, Jiang H, Gu D, et al. Radiomics in Liver Diseases: current progress and future opportunities. Liver International: Official Journal of the International Association for the Study of the Liver. 2020;40(9):2050\u201363.","journal-title":"Liver International: Official Journal of the International Association for the Study of the Liver"},{"key":"1172_CR34","doi-asserted-by":"publisher","first-page":"j5910","DOI":"10.1136\/bmj.j5910","volume":"360","author":"L Zhang","year":"2018","unstructured":"Zhang L, Wang H, Li Q, Zhao MH, Zhan QM. Big data and medical research in China. BMJ (Clinical Research ed). 2018;360:j5910.","journal-title":"BMJ (Clinical Research ed)"},{"issue":"3","key":"1172_CR35","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1002\/hep.31103","volume":"71","author":"A Spann","year":"2020","unstructured":"Spann A, Yasodhara A, Kang J, et al. Applying machine learning in Liver Disease and transplantation: a Comprehensive Review. Hepatology (Baltimore MD). 2020;71(3):1093\u2013105.","journal-title":"Hepatology (Baltimore MD)"},{"issue":"16","key":"1172_CR36","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1200\/JCO.19.03350","volume":"38","author":"AB Simon","year":"2020","unstructured":"Simon AB, Vitzthum LK, Mell LK. Challenge of directly comparing imaging-based diagnoses made by machine learning algorithms with those made by human clinicians. J Clin Oncology: Official J Am Soc Clin Oncol. 2020;38(16):1868\u20139.","journal-title":"J Clin Oncology: Official J Am Soc Clin Oncol"},{"issue":"4","key":"1172_CR37","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.jmir.2019.09.005","volume":"50","author":"G Currie","year":"2019","unstructured":"Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiation Sci. 2019;50(4):477\u201387.","journal-title":"J Med Imaging Radiation Sci"},{"issue":"4","key":"1172_CR38","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","volume":"18","author":"JG Lee","year":"2017","unstructured":"Lee JG, Jun S, Cho YW, et al. Deep learning in Medical Imaging: General Overview. Korean J Radiol. 2017;18(4):570\u201384.","journal-title":"Korean J Radiol"},{"issue":"5","key":"1172_CR39","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","volume":"26","author":"L Shao","year":"2015","unstructured":"Shao L, Zhu F, Li X. Transfer learning for visual categorization: a survey. IEEE Trans Neural Networks Learn Syst. 2015;26(5):1019\u201334.","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"1172_CR40","doi-asserted-by":"publisher","first-page":"110511","DOI":"10.1016\/j.asoc.2023.110511","volume":"144","author":"N Ghassemi","year":"2023","unstructured":"Ghassemi N, Shoeibi A, Khodatars M, et al. Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Appl Soft Comput. 2023;144:110511.","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1172_CR41","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.jhep.2021.05.025","volume":"75","author":"EASL Clinical Practice","year":"2021","unstructured":"EASL Clinical Practice. Guidelines on non-invasive tests for evaluation of Liver Disease severity and prognosis \u2013\u20092021 update. J Hepatol. 2021;75(3):659\u201389.","journal-title":"J Hepatol"},{"issue":"1","key":"1172_CR42","doi-asserted-by":"publisher","first-page":"21925","DOI":"10.1038\/s41598-022-25843-6","volume":"12","author":"A Nogami","year":"2022","unstructured":"Nogami A, Yoneda M, Iwaki M, et al. Diagnostic comparison of vibration-controlled transient elastography and MRI techniques in overweight and obese patients with NAFLD. Sci Rep. 2022;12(1):21925.","journal-title":"Sci Rep"},{"key":"1172_CR43","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. Therapeutic Adv Gastroenterol. 2021;14:17562848211062807.","journal-title":"Therapeutic Adv Gastroenterol"},{"issue":"1","key":"1172_CR44","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s41746-021-00544-y","volume":"5","author":"S Jayakumar","year":"2022","unstructured":"Jayakumar S, Sounderajah V, Normahani P, et al. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. NPJ Digit Med. 2022;5(1):11.","journal-title":"NPJ Digit Med"},{"key":"1172_CR45","doi-asserted-by":"publisher","first-page":"101662","DOI":"10.1016\/j.eclinm.2022.101662","volume":"53","author":"HL Xu","year":"2022","unstructured":"Xu HL, Gong TT, Liu FH, et al. Artificial intelligence performance in image-based Ovarian cancer identification: a systematic review and meta-analysis. EClinicalMedicine. 2022;53:101662.","journal-title":"EClinicalMedicine"},{"key":"1172_CR46","doi-asserted-by":"publisher","first-page":"1172451","DOI":"10.3389\/fcvm.2023.1172451","volume":"10","author":"MJ Wu","year":"2023","unstructured":"Wu MJ, Wang WQ, Zhang W, Li JH, Zhang XW. The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis. Front Cardiovasc Med. 2023;10:1172451.","journal-title":"Front Cardiovasc Med"},{"issue":"2","key":"1172_CR47","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s00264-022-05628-2","volume":"47","author":"A Korneev","year":"2023","unstructured":"Korneev A, Lipina M, Lychagin A, et al. Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon? Int Orthop. 2023;47(2):393\u2013403.","journal-title":"Int Orthop"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01172-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-01172-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-01172-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T03:03:33Z","timestamp":1702436613000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-01172-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1172"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-01172-6","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,11]]},"assertion":[{"value":"27 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article did not involve human or animal subjects, and the ethics statement and consent to participates were not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"208"}}