[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126281-en":3,"doc-seo-126281-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126281,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Validation of machine learning angiography-derived physiological pattern of coronary artery disease","A study validates machine-learning classification of coronary artery disease (CAD) physiological patterns using virtual pullbacks from single-view Murray’s law-based quantitative flow ratio (μFR) analysis. The pullback pressure gradient index (PPGi) cut-off of 0.78 differentiates focal from diffuse and non-focal disease, but penalized logistic regression and random forest models using multivariate μFR features outperform the binary PPGi approach. Expert panel interpretations served as reference across 343 vessels from 291 patients. Model results show higher accuracy and improved robustness across study populations.","European Heart Journal-Digital Health (2025) 6, 577–586  \nORIGINAL ARTICLE  \n[https://doi.org/10.1093/ehjdh/ztaf031](https://doi.org/10.1093/ehjdh/ztaf031 Artificial intelligence)[ Artificial intelligence](https://doi.org/10.1093/ehjdh/ztaf031 Artificial intelligence) (machine learning, deep learning)  \nValidation of machine learning angiography-derived physiological pattern of coronary artery disease  \nYueyun Zhu1,†, Simone Fezzi  2,3,†, Norma Bargary4, Daixin Ding2,3,4,5,6,7,8, Roberto Scarsini3, Mattia Lunardi5, Antonio Maria Leone6, Concetta Mammone3, Max Wagener1, Angela McInerney1, Gabor Toth7, Gabriele Pesarini3,  \nDavid Connolly2, Carlo Trani5, Shengxian Tu8, Flavio Ribichini3, Francesco Burzotta5, William Wijns2,*, and Andrew J. Simpkin  1,*  \n1School of Mathematical and Statistical Sciences, University of Galway, University Road, Galway H91 TK33, Ireland; 2The Lambe Institute for Translational Medicine, The Smart Sensors Laboratory and Curam, University of Galway, University Road, Galway H91 TK33, Ireland; 3Division of Cardiology, Department of Medicine, University of Verona, Verona, Italy; 4Department of Mathematics and Statistics, University of Limerick, Castletroy, Limerick V94 T9PX, Ireland; 5Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; 6Center of Excellence in Cardiovascular Sciences, Ospedale Isola Tiberina, Gemelli Isola, Università Cattolica del Sacro Cuore, Roma, Italy; 7Department of Cardiology, University Heart Center Graz, Medical University Graz, Graz, Austria; and 8School of Biomedical Engineering, Biomedical Instrument Institute, Shanghai Jiao Tong University, Shanghai, China  \nReceived 14 October 2024; revised 11 February 2025; accepted 4 March 2025; online publish-ahead-of-print 8 April 2025  \nAims The classification of physiological patterns of coronary artery disease (CAD) is crucial for clinical decision-making, significant  \nly affecting the planning and success of percutaneous coronary interventions (PCIs) . This study aimed to develop a novel index to reliably interpret and classify physiological CAD patterns based on virtual pullbacks from single-view Murray’slaw-based quantitative flow ratio (μFR) analysis.  \nMethods and results  \nThe pullback pressure gradient index (PPGi) was used to classify CAD patterns, with a cut-off value of PPGi = 0.78 distinguishing focal from diffuse and non-focal disease. The machine learning methods using penalized logistic regression and random forest were proposed to assess CAD patterns. Scores derived from multivariate functional principal component analysis of μFR and quantitative coronary analysis improved model performance. Expert panel interpretations served asthe reference. A total of 343 vessels (291 patients) underwent classification. The PPGi cut-off of 0.78 achieved 67% accuracy [95% confidence interval (CI): 66–68%] for focal vs. diffuse and 76% accuracy (95% CI: 75–76%) for focal vs. non-focal classification. The penalized logistic regression model, including PPGi as a feature, provided superior accuracy: 88%(95% CI: 87– 88%) for focal vs. diffuse and 81%(95% CI: 80–81%) for focal vs. non-focal classification. Moreover, the random forest model with PPGi as one of the features was applied for multiclass classification, providing an accuracy of 73%(95% CI: 73–73%) .  \nConclusion The machine learning models for physiological patterns of CAD classification outperformed the binary PPGi method, providing robust and generalizable classification across different study populations.  \nKeywords Coronary physiology • Machine learning models • Physiological pattern of disease • Percutaneous coronary intervention  \nDownloaded from by Universita Cattolica del Sacro Cuore user on 29 July[https://academic.oup.com/ehjdh/article/6/4/577/8107988](https://academic.oup.com/ehjdh/article/6/4/577/8107988) 2025  \n* Corresponding author. Tel: +353 91492331 (A.J.S.), Email: wi[l","cbCairLmcxFyIvQ1","https://ap.wps.com/l/cbCairLmcxFyIvQ1","pdf",1701717,6,1,10,"English","en",105,"# Aims\n# Methods and results\n## Classification approaches\n## Performance and reference standard\n# Conclusion\n# Introduction","[{\"question\":\"研究的主要目标是什么？\",\"answer\":\"研究旨在开发一种用于可靠解释与分类冠状动脉疾病（CAD）生理表型的新指标，并基于单视角μFR虚拟回撤分析来实现。\"},{\"question\":\"PPGi的判别阈值是多少，用于区分哪些病变类型？\",\"answer\":\"PPGi阈值为0.78：用于区分局灶型（focal）与弥漫型（diffuse），以及局灶型与非局灶型（non-focal）病变。\"},{\"question\":\"机器学习模型的表现如何，是否优于仅使用PPGi的方法？\",\"answer\":\"是的。带有PPGi特征的惩罚性逻辑回归模型与使用PPGi等特征的随机森林模型在准确性方面优于二分类PPGi方法，并提供更稳健的跨队列泛化能力。\"}]","Validation of machine learning angiography-derived physiological pattern of coronary artery disease | PDF",1785904239,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"validation-of-machine-learning-angiography-derived-physiological-pattern-of-coronary-artery-disease","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/validation-of-machine-learning-angiography-derived-physiological-pattern-of-coronary-artery-disease/126281/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"研究的主要目标是什么？","Question",{"text":77,"@type":78},"研究旨在开发一种用于可靠解释与分类冠状动脉疾病（CAD）生理表型的新指标，并基于单视角μFR虚拟回撤分析来实现。","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"PPGi的判别阈值是多少，用于区分哪些病变类型？",{"text":82,"@type":78},"PPGi阈值为0.78：用于区分局灶型（focal）与弥漫型（diffuse），以及局灶型与非局灶型（non-focal）病变。",{"name":84,"@type":75,"acceptedAnswer":85},"机器学习模型的表现如何，是否优于仅使用PPGi的方法？",{"text":86,"@type":78},"是的。带有PPGi特征的惩罚性逻辑回归模型与使用PPGi等特征的随机森林模型在准确性方面优于二分类PPGi方法，并提供更稳健的跨队列泛化能力。","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]