[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122722-en":3,"doc-seo-122722-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122722,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Applications of machine learning in familial hypercholesterolemia - review","Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolism disorder that elevates low-density lipoprotein cholesterol and increases the risk of early cardiovascular disease. Low screening and diagnostic rates often delay intervention, making early identification and management critical. This review summarizes how machine learning can support FH screening, diagnosis, and risk assessment using multiple data sources, including electronic health records, plasma lipid profiles, and corneal radian images, and outlines future efforts to improve model performance and clinical accuracy.","TYPE Review  \nPUBLISHED 26 September 2023 DOI 10.3389/fcvm.2023.1237258  \nEDITED BY  \nKailash Gulshan,  \nCleveland State University, United States  \nREVIEWED BY  \nLee Pyles,  \nWest Virginia University, United States José Pablo Miramontes González, Hospital Universitario Río Hortega, Spain  \n*CORRESPONDENCE  \nLong Jiang  \n [skyiadx@hotmail.com](skyiadx@hotmail.com)  \n†These authors share first authorship RECEIVED 09 June 2023  \nACCEPTED 11 September 2023  \nPUBLISHED 26 September 2023  \nCITATION  \nLuo R-F, Wang J-H, Hu L-J, Fu Q-A, Zhang S-Y and Jiang L (2023) Applications of machine learning in familial hypercholesterolemia.  \nFront. Cardiovasc. Med. 10:1237258 .  \ndoi: 10.3389/fcvm.2023.1237258  \nCOPYRIGHT  \n© 2023 Luo, Wang, Hu, Fu, Zhang and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nApplications of machine learning in familial hypercholesterolemia  \nRen-Fei Luo1†, Jing-Hui Wang1,2†, Li-Juan Hu3, Qing-An Fu1, Si-Yi Zhang2 and Long Jiang1*  \n1Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, China, 2Department of Clinical Medicine, Nanchang University Queen Mary School, Nanchang, China, 3Department of Nursing, Nanchang Medical College, Nanchang, China  \nFamilial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.  \nKEYWORDS  \nfamilial hypercholesterolemia, machine learning, screening, diagnosis, risk assessment  \n1. Introduction  \nFamilial hypercholesterolemia (FH) is a common autosomal dominant disease that isan inherited metabolic disorder (1) . The main characteristic of FH is abnormally high levels of low-density lipoprotein cholesterol (LDL-C) in plasma, resulting in an increased risk of early-onset atherosclerosis and premature cardiovascular disease (1, 2) . Heterozygous FH (HeFH) has a prevalence of 1 in 200–500 persons. Despite high incidence rate, the global diagnostic rate still remains low, and in most countries only 1% FH patients are diagnosed (3, 4) . Homozygous FH (HoFH) is rarer but more severe, with an estimated prevalence of 1 in 300,000–360,000 persons, and it involves higher LDL-C levels and physical signs, such as the early presence of cholesterol deposits on the skin, eyes, and tendons (3, 5) . Although there has been great progress in the study of FH, some challenges remain. For example, statins and other lipidlowering therapies have been widely used, and the detection and treatment of FH is still unsatisfactory (1, 6) . Missed diagnosis at an early age can lead to severe cardio","cbCaicED8ngUEEX0","https://ap.wps.com/l/cbCaicED8ngUEEX0","pdf",1086524,1,9,"English","en",105,"# Introduction\n## Familial hypercholesterolemia overview\n## Artificial intelligence and machine learning in cardiovascular medicine","[{\"question\":\"Why is early screening and diagnosis of familial hypercholesterolemia important?\",\"answer\":\"FH commonly causes elevated LDL cholesterol and increases the risk of early-onset atherosclerosis. Low diagnostic rates often delay early intervention, which may lead to severe cardiovascular events.\"},{\"question\":\"Which data sources can machine learning use for FH screening, diagnosis, and risk assessment?\",\"answer\":\"The review highlights electronic health records, plasma lipid profiles, and corneal radian images as different data sources that can feed machine learning models.\"},{\"question\":\"What future directions does the review emphasize for machine learning in FH?\",\"answer\":\"Future research should develop machine learning models with better performance and accuracy. Improved models are expected to provide more reliable prediction, diagnosis, and management tools for early FH treatment.\"}]","Applications of machine learning in familial hypercholesterolemia - review | PDF",1785812533,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"applications-of-machine-learning-in-familial-hypercholesterolemia-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/applications-of-machine-learning-in-familial-hypercholesterolemia-review/122722/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early screening and diagnosis of familial hypercholesterolemia important?","Question",{"text":75,"@type":76},"FH commonly causes elevated LDL cholesterol and increases the risk of early-onset atherosclerosis. Low diagnostic rates often delay early intervention, which may lead to severe cardiovascular events.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources can machine learning use for FH screening, diagnosis, and risk assessment?",{"text":80,"@type":76},"The review highlights electronic health records, plasma lipid profiles, and corneal radian images as different data sources that can feed machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"What future directions does the review emphasize for machine learning in FH?",{"text":84,"@type":76},"Future research should develop machine learning models with better performance and accuracy. 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