{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:35:05Z","timestamp":1793061305193,"version":"4.5.0"},"reference-count":56,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["cell.com","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Cell Reports Medicine"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.xcrm.2026.102971","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:12:53Z","timestamp":1786115573000},"page":"102971","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics"],"prefix":"10.1016","volume":"7","author":[{"given":"Yuanxu","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Ke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohui","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Winston","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manson","family":"Fok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Beck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Io Nam","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-1697","authenticated-orcid":false,"given":"Kang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.xcrm.2026.102971_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.aohep.2023.101133","article-title":"A multisociety Delphi consensus statement on new fatty liver disease nomenclature","volume":"29","author":"Rinella","year":"2024","journal-title":"Ann. Hepatol."},{"key":"10.1016\/j.xcrm.2026.102971_bib2","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/S0140-6736(24)01811-7","article-title":"Steatotic liver disease","volume":"404","author":"Israelsen","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.xcrm.2026.102971_bib3","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1038\/s41575-024-00967-4","article-title":"Liver diseases and hepatocellular carcinoma in the Asia-pacific region: burden, trends, challenges and future directions","volume":"21","author":"Mak","year":"2024","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"10.1016\/j.xcrm.2026.102971_bib4","doi-asserted-by":"crossref","first-page":"e79","DOI":"10.1016\/j.jhep.2023.09.014","article-title":"Metabolic dysfunction-associated steatotic liver disease and all-cause\/cause-specific mortality among adults in the United States","volume":"80","author":"Kim","year":"2024","journal-title":"J. Hepatol."},{"key":"10.1016\/j.xcrm.2026.102971_bib5","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/S2468-1253(25)00097-4","article-title":"Screening for advanced steatotic liver disease","volume":"10","author":"Johansen","year":"2025","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"10.1016\/j.xcrm.2026.102971_bib6","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41588-018-0057-4","article-title":"Genome-wide mapping of global-to-local genetic effects on human facial shape","volume":"50","author":"Claes","year":"2018","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xcrm.2026.102971_bib7","article-title":"AI-enabled molecular phenotyping and prognostic predictions in lung cancer through multimodal clinical information integration","volume":"6","author":"Lu","year":"2025","journal-title":"CR Med"},{"key":"10.1016\/j.xcrm.2026.102971_bib8","article-title":"A foundational architecture for AI agents in healthcare","volume":"6","author":"Liu","year":"2025","journal-title":"CR Med"},{"key":"10.1016\/j.xcrm.2026.102971_bib9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","article-title":"The practical implementation of artificial intelligence technologies in medicine","volume":"25","author":"He","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.xcrm.2026.102971_bib10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41591-018-0279-0","article-title":"Identifying facial phenotypes of genetic disorders using deep learning","volume":"25","author":"Gurovich","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.xcrm.2026.102971_bib11","article-title":"FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication: a model development and validation study","volume":"7","author":"Bontempi","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.xcrm.2026.102971_bib12","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1038\/s42255-020-00270-x","article-title":"Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle","volume":"2","author":"Xia","year":"2020","journal-title":"Nat. Metab."},{"key":"10.1016\/j.xcrm.2026.102971_bib13","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1038\/s41597-025-04922-z","article-title":"An explainable dataset linking facial phenotypes and genes to rare genetic diseases","volume":"12","author":"Song","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.xcrm.2026.102971_bib14","first-page":"1","article-title":"FPGDKG 1.0: an integrated facial phenotype-gene-disease knowledge graph for rare disease diagnosis and explanation","author":"He","year":"2026","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.xcrm.2026.102971_bib15","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1038\/s41746-025-01955-x","article-title":"Graph retrieval augmented large language models for facial phenotype associated rare genetic disease","volume":"8","author":"Song","year":"2025","journal-title":"npj Digit. Med."},{"key":"10.1016\/j.xcrm.2026.102971_bib16","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1016\/j.cmet.2024.05.012","article-title":"Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases","volume":"36","author":"Yu","year":"2024","journal-title":"Cell Metab."},{"key":"10.1016\/j.xcrm.2026.102971_bib17","article-title":"AI-assisted facial analysis in healthcare: From disease detection to comprehensive management","volume":"6","author":"Lei","year":"2025","journal-title":"Patterns (N Y)"},{"key":"10.1016\/j.xcrm.2026.102971_bib18","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10462-025-11246-2","article-title":"Face-based machine learning diagnostics: applications, challenges and opportunities","volume":"58","author":"Song","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.xcrm.2026.102971_bib19","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","article-title":"Multimodal biomedical AI","volume":"28","author":"Acosta","year":"2022","journal-title":"Nat. Med."},{"key":"10.1016\/j.xcrm.2026.102971_bib20","article-title":"Learning Transferable Visual Models From Natural Language Supervision","author":"Radford","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib21","doi-asserted-by":"crossref","first-page":"12598","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.xcrm.2026.102971_bib22","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1186\/s12967-016-1046-y","article-title":"China suboptimal health cohort study: rationale, design and baseline characteristics","volume":"14","author":"Wang","year":"2016","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.xcrm.2026.102971_bib23","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.imavis.2013.03.001","article-title":"A computational approach to body mass index prediction from face images","volume":"31","author":"Wen","year":"2013","journal-title":"Image Vis Comput."},{"key":"10.1016\/j.xcrm.2026.102971_bib24","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1609\/icwsm.v11i1.14923","article-title":"Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media","volume":"11","author":"Kocabey","year":"2017","journal-title":"Proceedings of the International AAAI Conference on Web and Social Media"},{"key":"10.1016\/j.xcrm.2026.102971_bib25","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.imavis.2019.07.003","article-title":"On visual BMI analysis from facial images","volume":"89","author":"Jiang","year":"2019","journal-title":"Image Vis Comput."},{"key":"10.1016\/j.xcrm.2026.102971_bib26","doi-asserted-by":"crossref","first-page":"102985","DOI":"10.1016\/j.cviu.2020.102985","article-title":"Visual BMI estimation from face images using a label distribution based method","volume":"197","author":"Jiang","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.xcrm.2026.102971_bib27","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/j.jhep.2018.01.019","article-title":"Fructose and sugar: A major mediator of non-alcoholic fatty liver disease","volume":"68","author":"Jensen","year":"2018","journal-title":"J. Hepatol."},{"key":"10.1016\/j.xcrm.2026.102971_bib28","series-title":"Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security","first-page":"1322","article-title":"Model inversion attacks that exploit confidence information and basic countermeasures","author":"Fredrikson","year":"2015"},{"key":"10.1016\/j.xcrm.2026.102971_bib29","first-page":"267","article-title":"The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks","author":"Carlini","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib30","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1145\/2976749.2978318","article-title":"Deep Learning with Differential Privacy","volume":"16","author":"Abadi","year":"2016","journal-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS"},{"key":"10.1016\/j.xcrm.2026.102971_bib31","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1016\/S0140-6736(20)32511-3","article-title":"Non-alcoholic fatty liver disease","volume":"397","author":"Powell","year":"2021","journal-title":"Lancet"},{"key":"10.1016\/j.xcrm.2026.102971_bib32","doi-asserted-by":"crossref","DOI":"10.3748\/wjg.v32.i2.111737","article-title":"Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches","volume":"32","author":"Hern\u00e1ndez-Almonacid","year":"2026","journal-title":"World J. Gastroenterol."},{"key":"10.1016\/j.xcrm.2026.102971_bib33","article-title":"Metabolic fingerprinting enables rapid, label-free histopathology in gastric cancer diagnosis and prognostic prediction","volume":"6","author":"Teng","year":"2025","journal-title":"CR Med"},{"key":"10.1016\/j.xcrm.2026.102971_bib34","doi-asserted-by":"crossref","DOI":"10.1002\/VIW.20250157","article-title":"A multimodal deep learning framework for predicting sunitinib response in advanced clear cell renal cell carcinoma","volume":"7","author":"Tian","year":"2026","journal-title":"View"},{"key":"10.1016\/j.xcrm.2026.102971_bib35","doi-asserted-by":"crossref","DOI":"10.1002\/mef2.43","article-title":"Accelerating the integration of ChatGPT and other large-scale AI models into biomedical research and healthcare","volume":"2","author":"Wang","year":"2023","journal-title":"MedComm - Future Medicine"},{"key":"10.1016\/j.xcrm.2026.102971_bib36","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1038\/s42256-024-00944-1","article-title":"LLM-based agentic systems in medicine and healthcare","volume":"6","author":"Qiu","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.xcrm.2026.102971_bib37","doi-asserted-by":"crossref","DOI":"10.1002\/mef2.49","article-title":"The inevitable transformation of medicine and research by large language models: The possibilities and pitfalls","volume":"2","author":"Gao","year":"2023","journal-title":"MedComm - Future Medicine"},{"key":"10.1016\/j.xcrm.2026.102971_bib38","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1016\/j.jhep.2023.06.003","article-title":"A multisociety delphi consensus statement on new fatty liver disease nomenclature","volume":"79","author":"Rinella","year":"2023","journal-title":"J. Hepatol."},{"key":"10.1016\/j.xcrm.2026.102971_bib39","series-title":"Multi-view Consensus CNN for 3D Facial Landmark Placement","first-page":"706","author":"Paulsen","year":"2018"},{"key":"10.1016\/j.xcrm.2026.102971_bib40","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"10.1016\/j.xcrm.2026.102971_bib41","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/0004-3702(81)90020-5","article-title":"The visual interpretation of surface contours","volume":"17","author":"Stevens","year":"1981","journal-title":"Artif. Intell."},{"key":"10.1016\/j.xcrm.2026.102971_bib42","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib43","first-page":"945","article-title":"Multi-view convolutional neural networks for 3d shape recognition","author":"Su","year":"2015","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib44","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1136\/gutjnl-2015-309265","article-title":"Screening diabetic patients for non-alcoholic fatty liver disease with controlled attenuation parameter and liver stiffness measurements: a prospective cohort study","volume":"65","author":"Kwok","year":"2016","journal-title":"Gut"},{"key":"10.1016\/j.xcrm.2026.102971_bib45","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"Kendall","year":"2018","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib46","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.xcrm.2026.102971_bib47","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1038\/nprot.2011.335","article-title":"Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry","volume":"6","author":"Dunn","year":"2011","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.xcrm.2026.102971_bib48","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1038\/nm.4358","article-title":"Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention","volume":"23","author":"Liu","year":"2017","journal-title":"Nat. Med."},{"key":"10.1016\/j.xcrm.2026.102971_bib49","doi-asserted-by":"crossref","first-page":"W486","DOI":"10.1093\/nar\/gky310","article-title":"MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis","volume":"46","author":"Chong","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.xcrm.2026.102971_bib50","article-title":"MediaPipe: a framework for building perception pipelines","author":"Lugaresi","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib51","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib52","article-title":"Inverting Gradients--How easy is it to break privacy in federated learning?","author":"Geiping","year":"2020","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib53","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy. Found","volume":"9","author":"Dwork","year":"2014","journal-title":"Trends Theor. Comput. Sci."},{"key":"10.1016\/j.xcrm.2026.102971_bib54","first-page":"16337","article-title":"See through Gradients: Image Batch Recovery via GradInversion","author":"Yin","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2026.102971_bib55","series-title":"Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library","author":"Kaehler","year":"2016"},{"key":"10.1016\/j.xcrm.2026.102971_bib56","article-title":"Multi-style generative network for real-time transfer","author":"Zhang","year":"2018","journal-title":"arXiv"}],"container-title":["Cell Reports Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666379126003885?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666379126003885?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T00:23:22Z","timestamp":1793060602000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666379126003885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":56,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["S2666379126003885"],"URL":"https:\/\/doi.org\/10.1016\/j.xcrm.2026.102971","relation":{},"ISSN":["2666-3791"],"issn-type":[{"value":"2666-3791","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics","name":"articletitle","label":"Article Title"},{"value":"Cell Reports Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xcrm.2026.102971","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"102971"}}