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Ten board-certified cardiologists from multiple centers interpreted angiograms from 180 patients with marked stenosis. Usability was measured with the System Usability Scale, while agreement and correlation versus manual QCA were quantified with statistical correlation methods and frame-selection accuracy. AI-QCA showed marginally high acceptability, faster analysis times, and moderate-to-strong correlations across key angiographic variables, supporting feasibility in cath lab workflow.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/cardiologist-user-experience-of-artificial-intelligence-based-quantitative-coronary-angiography-research-findings/450344/",{"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/cardiologist-user-experience-of-artificial-intelligence-based-quantitative-coronary-angiography-research-findings/450344.png","ImageObject",300,407,{"name":92,"@type":93},"Margaret","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-07","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What technology was evaluated in this study?","Question",{"text":112,"@type":113},"The study evaluated artificial intelligence-assisted quantitative coronary angiography (AI-QCA), designed to automate objective assessment of coronary artery stenosis with minimal human intervention.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How many cardiologists and patients were included?",{"text":117,"@type":113},"Ten board-certified cardiologists analyzed angiograms from 180 patients with marked coronary stenosis requiring revascularization.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main findings about usability and analysis time?",{"text":121,"@type":113},"The average System Usability Scale (SUS) score was 66.7, indicating marginal high acceptability. Cardiologists’ AI-QCA-assisted analysis time was 1.5±0.9 seconds, significantly lower than manual QCA by an expert analyst (88.1±35.5 seconds, P\u003C0.001).",{"name":123,"@type":110,"acceptedAnswer":124},"How did AI-QCA compare with manual QCA for angiographic measurements?",{"text":125,"@type":113},"Key angiographic variables such as reference vessel diameter, minimal lumen diameter, diameter stenosis, and lesional length showed moderate-to-strong correlations between AI-QCA and manual QCA.","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},450344,1791210460,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":147,"read_time":148},137451207643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Cardiologist user experience of artificial intelligence-based quantitative coronary angiography  \nOhchul Kwon 1 ^, Hyuck-Jun Yoon2 , Jung-Hee Lee3 , Jun Hwan Cho4 , Yongcheol Kim 5 , Jon Suh6 , Sang Yeub Lee4, In Tae Moon7, Donghoon Han8, Jang Hoon Lee9, Ho-Jun Jang10^, Si-Hyuck Kang1^  \n1Cardiovascular Center, Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea; 2Division of Cardiology, Department of Internal Medicine, Keimyung University Dongsan Hospital, Daegu, Republic of Korea; 3Division of Cardiology, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea; 4Division of Cardiology, Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Republic of Korea; 5Yonsei University College of Medicine and Cardiovascular Center, Yongin Severance Hospital, Yongin, Republic of Korea; 6Department of Cardiology, Soon Chun Hyang University Hospital Bucheon, Bucheon, Republic of Korea; 7Division of Cardiology, Department of Internal Medicine, Uijeongbu Eulji University Hospital, Uijeongbu, Republic of Korea; 8Division of Cardiology, Department of Internal Medicine, Kangnam Sacred Heart Hospital, Seoul, Republic of Korea; 9Division of Cardiology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea; 10Division of Cardiology, Department of Internal Medicine, Bucheon Sejong Hospital, Bucheon, Republic of Korea  \nContributions: (I) Conception and design: SH Kang; (II) Administrative support: O Kwon, SH Kang; (III) Provision of study materials or patients: HJ Yoon, Jung Hee Lee, JH Cho, Y Kim, J Suh, SY Lee, IT Moon, D Han, Jang Hoon Lee, HJ Jang; (IV) Collection and assembly of data: SH Kang; (V) Data analysis and interpretation: O Kwon; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.  \nCorrespondence to: Si-Hyuck Kang, MD, PhD. Associate Professor, Cardiovascular Center, Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gumiro 166, Bundang, Seongnam, Gyeonggi-do, Republic of Korea. Email: [eandp303@gmail.com](eandp303@gmail.com).  \nBackground: Artificial intelligence-assisted quantitative coronary angiography (AI-QCA) has been developed to enable the automated, objective assessment of coronary artery stenosis without human intervention. Previous studies have shown its accuracy compared with manual QCA and intravascular ultrasound. In this study, we aimed to evaluate cardiologists’ experience of analyzing coronary lesions with  \nAI-QCA.  \nMethods: Ten board-certified cardiologists from multiple centers specializing in coronary intervention, with varying periods of experience, participated in this study. They analyzed angiograms from 180 patients with marked coronary stenosis requiring coronary revascularization. Correlations between manual QCA and  \nAI-QCA were measured by using Pearson’s or Spearman’s correlation coefficients.  \nResults: The average System Usability Scale (SUS) score was 66.7, indicating marginal high acceptability.  \nThe angiographic frame selected by the cardiologists with AI-QCA assistance was within five frames of that elected by the QCA analyst in 64.2% of cases. Furthermore, the time taken by cardiologists to analyze angiograms with AI-QCA assistance was 1.5±0.9 s, significantly lower than that required by an expert analyst to perform manual QCA (88.1±35.5 s, P\u003C0.001). Key angiographic variables, such as reference vessel diameter (RD), minimal lumen diameter (MLD), diameter stenosis (DS), and lesional length (LL), showed moderate-to-strong correlations between AI-QCA and manual QCA (e.g., distal reference diameter, R=0.74) .  \nConclusions: This prospective study showed that automated analysis with AI-Q","cbCaig0GyLbwymBO","https://ap.wps.com/l/cbCaig0GyLbwymBO","pdf",1061487,"English","# Introduction\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What technology was evaluated in this study?\",\"answer\":\"The study evaluated artificial intelligence-assisted quantitative coronary angiography (AI-QCA), designed to automate objective assessment of coronary artery stenosis with minimal human intervention.\"},{\"question\":\"How many cardiologists and patients were included?\",\"answer\":\"Ten board-certified cardiologists analyzed angiograms from 180 patients with marked coronary stenosis requiring revascularization.\"},{\"question\":\"What were the main findings about usability and analysis time?\",\"answer\":\"The average System Usability Scale (SUS) score was 66.7, indicating marginal high acceptability. Cardiologists’ AI-QCA-assisted analysis time was 1.5±0.9 seconds, significantly lower than manual QCA by an expert analyst (88.1±35.5 seconds, P\\u003c0.001).\"},{\"question\":\"How did AI-QCA compare with manual QCA for angiographic measurements?\",\"answer\":\"Key angiographic variables such as reference vessel diameter, minimal lumen diameter, diameter stenosis, and lesional length showed moderate-to-strong correlations between AI-QCA and manual QCA.\"}]","Cardiologist user experience of artificial intelligence-based quantitative coronary angiography - Research Findings | PDF",1790732935,23]