[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122291-en":3,"doc-seo-122291-105":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},122291,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events - research article","Machine learning-based computational analysis is evaluated for extracting scar microstructure features from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) to improve prediction of major arrhythmic events in stable coronary artery disease. Prospective CMR registry data from 397 patients are used to quantify shape-based peri-infarct and core fibrosis characteristics and to compare an ensemble ML classifier with guideline-representative Cox modeling. Peri-infarct zone entropy, peri-infarct components, and core interface area outperform standard care parameters with superior AUROC and remain significant in multivariate analysis after adjustment for LVEF and NYHA class.","TYPE Original Research PUBLISHED 07 February 2023 DOI 10.3389/fcvm.2023.1082778  \nOPEN ACCESS  \nEDITED BY  \nLuis Lopes,  \nUniversity College London, United Kingdom  \nREVIEWED BY  \nRhodri Davies,  \nUniversity College London, United Kingdom  \nSílvia Aguiar Rosa, Hospital de Santa Marta, Portugal  \n*CORRESPONDENCE  \nHassan A. Zaidi  \n [syed.h.zaidi@kcl.ac.uk](syed.h.zaidi@kcl.ac.uk)  \n†These authors have contributed equally to this work and share first authorship  \n‡These authors have contributed equally to this work and share senior authorship  \nSPECIALTY SECTION  \nThis article was submitted to Cardiovascular Imaging, a section of the journal  \nFrontiers in Cardiovascular Medicine  \nRECEIVED 28 October 2022  \nACCEPTED 13 January 2023  \nPUBLISHED 07 February 2023  \nCITATION  \nZaidi HA, Jones RE, Hammersley DJ, Hatipoglu S, Balaban G, Mach L, Halliday BP, Lamata P, Prasad SK and Bishop MJ (2023) Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events. Front. Cardiovasc. Med. 10:1082778 .  \ndoi: 10.3389/fcvm.2023.1082778  \nCOPYRIGHT  \n© 2023 Zaidi, Jones, Hammersley, Hatipoglu, Balaban, Mach, Halliday, Lamata, Prasad and Bishop. 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.  \nMachine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events  \nHassan A. Zaidi 1*†, Richard E. Jones 2, 3†, Daniel J. Hammersley 2, 3, Suzan Hatipoglu3, Gabriel Balaban 1, 4, Lukas Mach 2, 3,  \nBrian P. Halliday 2, 3, Pablo Lamata 1, Sanjay K. Prasad 2, 3‡ and Martin J. Bishop 1‡  \n1 Department of Biomedical Engineering, School of Biomedical and Imaging Sciences, King’s College London, London, United Kingdom, 2 National Heart and Lung Institute, Imperial College London, London, United Kingdom, 3Cardiovascular Magnetic Resonance Unit, Royal Brompton and Harefield Hospitals, Guy’sand St Thomas’ NHS Foundation Trust, London, United Kingdom, 4 Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway  \nBackground: Machine learning analysis of complex myocardial scar patterns affordsthe potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD) .  \nObjective: To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD.  \nMethods: Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous (‘periinfarct’) and homogeneous (‘core’) fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling.  \nResults: Of 397 patients (mean LVEF 45.4±16 .0) followed for a median of 6years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF\u003C35% and NYHA>Class I): AUROC (95%CI) 0.81 (0 .81– 0. 82) vs. 0.64 (0 .63–0.65), p =0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF\u003C35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1. 88, 95% confidence interval (CI) 1.38–2. 56, p\u003C0 .001; number of PIZ components HR 1.34, 95","cbCaird3F3tgVdsI","https://ap.wps.com/l/cbCaird3F3tgVdsI","pdf",2717728,1,11,"English","en",105,"# Introduction\n# Objective\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What clinical problem does this study address?\",\"answer\":\"It targets improved risk prediction for major arrhythmic events and sudden cardiac death beyond left ventricular ejection fraction–centered guideline strategies.\"},{\"question\":\"How were scar features derived in the study?\",\"answer\":\"The study extracted shape-based scar microstructure features from LGE-CMR, characterizing heterogeneous peri-infarct fibrosis and homogeneous core fibrosis.\"},{\"question\":\"Which machine learning features performed best?\",\"answer\":\"Peri-infarct zone entropy, peri-infarct components, and core interface area outperformed a model representative of current standard-of-care parameters.\"}]","Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events - research article | PDF",1785809847,28,{"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},"machine-learning-analysis-of-complex-late-gadolinium-enhancement-patterns-to-improve-risk-prediction-of-major-arrhythmic-events-research-article","",{"@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/machine-learning-analysis-of-complex-late-gadolinium-enhancement-patterns-to-improve-risk-prediction-of-major-arrhythmic-events-research-article/122291/",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},"What clinical problem does this study address?","Question",{"text":75,"@type":76},"It targets improved risk prediction for major arrhythmic events and sudden cardiac death beyond left ventricular ejection fraction–centered guideline strategies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were scar features derived in the study?",{"text":80,"@type":76},"The study extracted shape-based scar microstructure features from LGE-CMR, characterizing heterogeneous peri-infarct fibrosis and homogeneous core fibrosis.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning features performed best?",{"text":84,"@type":76},"Peri-infarct zone entropy, peri-infarct components, and core interface area outperformed a model representative of current standard-of-care parameters.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]