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An automated pipeline predicts abnormalities across four lipid indicators: triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol, using 1,757 liver images from 89 patients with contemporaneous blood testing. Transfer learning with ResNet18, MobileNet, DenseNet, AlexNet, and SqueezeNet is evaluated using 8-fold cross-validation and independent testing. The optimal ResNet18 model shows strong internal accuracy and reliable external performance, supporting use as an early warning tool.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-and-validation-of-an-artificial-intelligence-model-based-on-liver-cse-mri-fat-maps-for-predicting-dyslipidemia/455384/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-and-validation-of-an-artificial-intelligence-model-based-on-liver-cse-mri-fat-maps-for-predicting-dyslipidemia/455384.png","ImageObject",300,407,{"name":92,"@type":93},"acoobca","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of the AI model in this study?","Question",{"text":112,"@type":113},"To develop and validate a non-invasive AI model that predicts dyslipidemia by analyzing liver CSE-MRI fat maps for lipid abnormalities.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which lipid indicators are predicted from liver CSE-MRI fat maps?",{"text":117,"@type":113},"The model predicts abnormalities for triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol.",{"name":119,"@type":110,"acceptedAnswer":120},"How was the model trained and evaluated?",{"text":121,"@type":113},"Transfer learning was applied using several pre-trained networks (e.g., ResNet18, MobileNet, DenseNet, AlexNet, SqueezeNet). Performance was assessed using 8-fold cross-validation and an independent held-out test set with derived metrics.",{"name":123,"@type":110,"acceptedAnswer":124},"What clinical value does the study suggest for the approach?",{"text":125,"@type":113},"It may enable opportunistic screening: when patients undergo abdominal MRI for other reasons, existing scan data could be analyzed to assess dyslipidemia risk without additional cost or scan time.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},455384,1790815323,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},3985747870733,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","OPEN ACCESS  \nCitation: Jiang B, Situ W, Feng Z, Yuan J, Wang Y, Chen X, et al. (2026) Development and validation of an artificial intelligence model based on liver CSE-MRI fat maps for predicting dyslipidemia. PLOS Digit Health 5(1):  \ne0001119. [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pdig.0001119  \nEditor: Alexander Wong, University of Waterloo, CANADA  \nReceived: October 23, 2024  \nAccepted: November 17, 2025  \nPublished: January 7, 2026  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pdig.0001119  \nCopyright: © 2026 Jiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution,  \nRESEARCH ARTICLE  \nDevelopment and validation of an artificial intelligence model based on liver CSE-MRI fat maps for predicting dyslipidemia  \nBo Jiang1, Weijun Situ1, Zhichao Feng2, Jianmin Yuan2, Yina Wang3, Xiaofan Chen1, Xiong Wu1, Kai Deng1, Haitao Yang1, Xiao Xiao3, Xi Guo1, Junjiao Hu1,4*  \n1 Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, 2 Central Research Institute, United Imaging Healthcare, Shanghai, China, 3 Department of Geriatric Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, 4 The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shanxi, China  \n* [hujujiao91@csu.edu.cn](hujujiao91@csu.edu.cn)  \nAbstract  \nThis study aimed to develop and validate an artificial intelligence (AI) model for thenon-invasive early detection of dyslipidemia using liver chemical shift-encoded MRI (CSE-MRI) fat maps. An automated AI pipeline was developed to predict abnormalities in four lipid indicators: triglyceride, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. The study utilized 1,757 liver CSE-MRI fat images from 89 patients who underwent MRI scans and contemporaneous blood lipid testing. Transfer learning was applied using several pre-trained networks, including ResNet18, MobileNet, DenseNet, AlexNet, and SqueezeNet. Model performance was evaluated via 8-fold cross-validation, with the optimal model further assessed on a held-out test set using confusion matrices and derived metrics. Significant performance differences were observed among models. The optimal model, based on ResNet18, demonstrated high accuracy in the internal validation set. On the independent test set, this model achieved accuracies of 0.853 for triglyceride, 0.833 for total cholesterol, 0.937 for low-density lipoprotein cholesterol, and 0.936 for high-density lipoprotein cholesterol, with corresponding F1-Scores of 0.885, 0.571, 0.886, and 0.897. The AI model based on liver CSEMRI fat maps shows high accuracy and generalization in predicting abnormalities for three key lipid indices, validating its potential as an early warning tool for dyslipidemia. Expanding the training dataset could further enhance performance for all lipid indices.  \nPLOS Digital Health | [https://doi.org/10.1371/journal.pdig.0001119](https://doi.org/10.1371/journal.pdig.0001119) January 7, 2026 1 / 13  \nand reproduction in any medium, provided the original author and source are credited.  \nData availability statement: All data underlying the findings described in this manuscript are fully and publicly available without restriction. The dataset has been deposited on figshare, a public data repository, and can be accessed via the following Digital Object Identifier (DOI) and URL: DOI:  \n","cbCaidNePT99anTI","https://ap.wps.com/l/cbCaidNePT99anTI","pdf",1413067,13,"English","# Abstract\n## Introduction\n## Methods and Model Development\n## Model Evaluation and Results","[{\"question\":\"What is the purpose of the AI model in this study?\",\"answer\":\"To develop and validate a non-invasive AI model that predicts dyslipidemia by analyzing liver CSE-MRI fat maps for lipid abnormalities.\"},{\"question\":\"Which lipid indicators are predicted from liver CSE-MRI fat maps?\",\"answer\":\"The model predicts abnormalities for triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol.\"},{\"question\":\"How was the model trained and evaluated?\",\"answer\":\"Transfer learning was applied using several pre-trained networks (e.g., ResNet18, MobileNet, DenseNet, AlexNet, SqueezeNet). Performance was assessed using 8-fold cross-validation and an independent held-out test set with derived metrics.\"},{\"question\":\"What clinical value does the study suggest for the approach?\",\"answer\":\"It may enable opportunistic screening: when patients undergo abdominal MRI for other reasons, existing scan data could be analyzed to assess dyslipidemia risk without additional cost or scan time.\"}]","Development and validation of an artificial intelligence model based on liver CSE-MRI fat maps for predicting dyslipidemia | PDF",1790742746,33]