Jung In Jo, Hyun Jung Koo, Joon Won Kang, Young Hak Kim, Dong Hyun Yang
{"title":"人工智能驱动的冠状动脉 CT 血管造影对中度狭窄的评估:与定量冠状动脉造影和分数血流储备的比较","authors":"Jung In Jo, Hyun Jung Koo, Joon Won Kang, Young Hak Kim, Dong Hyun Yang","doi":"10.1016/j.amjcard.2024.12.011","DOIUrl":null,"url":null,"abstract":"<p><p>We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary CT angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR). This single-center retrospective study included 195 symptomatic patients (mean age 61 ± 10 years, 149 men, 585 coronary arteries) with 215 intermediate coronary lesions, with quantitative coronary angiography (QCA) diameter stenosis ranging from 20-80%. An AI-driven research prototype (AI-CCTA) was used to quantify stenosis on CCTA images. The diagnostic accuracy of AI-CCTA was assessed on a per-vessel basis using invasive coronary angiography stenosis grading (with ≥50% stenosis) or invasive FFR (≤0.80) as reference standards. AI-driven diameter stenosis was correlated with the QCA results and expert manual measurements subsequently. The disease prevalence among the 585 coronary arteries, as determined by invasive angiography (≥50%), was 46.5%. AI-CCTA exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) of 71.7%, 89.8%, 85.9%, 78.5%, and 0.81, respectively. The diagnostic performance of AI-CCTA was moderate for the 215 intermediate lesions assessed using QCA and FFR, with an AUC of 0.63 for QCA and FFR. AI-CCTA demonstrated a moderate correlation with QCA (r=0.42, p < 0.001) for measuring the degree of stenosis, which was notably better than the results from manual quantification versus QCA (r=0.26, p=0.001). In conclusion, AI-driven CCTA analysis exhibited promising results. AI-CCTA demonstrated a moderate relationship with QCA in intermediate coronary stenosis lesions; however, its results surpassed those of manual evaluations.</p>","PeriodicalId":7705,"journal":{"name":"American Journal of Cardiology","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence-Driven Assessment of Coronary CT Angiography for Intermediate Stenosis: Comparison with Quantitative Coronary Angiography and Fractional Flow Reserve.\",\"authors\":\"Jung In Jo, Hyun Jung Koo, Joon Won Kang, Young Hak Kim, Dong Hyun Yang\",\"doi\":\"10.1016/j.amjcard.2024.12.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary CT angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR). This single-center retrospective study included 195 symptomatic patients (mean age 61 ± 10 years, 149 men, 585 coronary arteries) with 215 intermediate coronary lesions, with quantitative coronary angiography (QCA) diameter stenosis ranging from 20-80%. An AI-driven research prototype (AI-CCTA) was used to quantify stenosis on CCTA images. The diagnostic accuracy of AI-CCTA was assessed on a per-vessel basis using invasive coronary angiography stenosis grading (with ≥50% stenosis) or invasive FFR (≤0.80) as reference standards. AI-driven diameter stenosis was correlated with the QCA results and expert manual measurements subsequently. The disease prevalence among the 585 coronary arteries, as determined by invasive angiography (≥50%), was 46.5%. AI-CCTA exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) of 71.7%, 89.8%, 85.9%, 78.5%, and 0.81, respectively. The diagnostic performance of AI-CCTA was moderate for the 215 intermediate lesions assessed using QCA and FFR, with an AUC of 0.63 for QCA and FFR. AI-CCTA demonstrated a moderate correlation with QCA (r=0.42, p < 0.001) for measuring the degree of stenosis, which was notably better than the results from manual quantification versus QCA (r=0.26, p=0.001). In conclusion, AI-driven CCTA analysis exhibited promising results. AI-CCTA demonstrated a moderate relationship with QCA in intermediate coronary stenosis lesions; however, its results surpassed those of manual evaluations.</p>\",\"PeriodicalId\":7705,\"journal\":{\"name\":\"American Journal of Cardiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Cardiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.amjcard.2024.12.011\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Cardiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.amjcard.2024.12.011","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Artificial Intelligence-Driven Assessment of Coronary CT Angiography for Intermediate Stenosis: Comparison with Quantitative Coronary Angiography and Fractional Flow Reserve.
We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary CT angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR). This single-center retrospective study included 195 symptomatic patients (mean age 61 ± 10 years, 149 men, 585 coronary arteries) with 215 intermediate coronary lesions, with quantitative coronary angiography (QCA) diameter stenosis ranging from 20-80%. An AI-driven research prototype (AI-CCTA) was used to quantify stenosis on CCTA images. The diagnostic accuracy of AI-CCTA was assessed on a per-vessel basis using invasive coronary angiography stenosis grading (with ≥50% stenosis) or invasive FFR (≤0.80) as reference standards. AI-driven diameter stenosis was correlated with the QCA results and expert manual measurements subsequently. The disease prevalence among the 585 coronary arteries, as determined by invasive angiography (≥50%), was 46.5%. AI-CCTA exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) of 71.7%, 89.8%, 85.9%, 78.5%, and 0.81, respectively. The diagnostic performance of AI-CCTA was moderate for the 215 intermediate lesions assessed using QCA and FFR, with an AUC of 0.63 for QCA and FFR. AI-CCTA demonstrated a moderate correlation with QCA (r=0.42, p < 0.001) for measuring the degree of stenosis, which was notably better than the results from manual quantification versus QCA (r=0.26, p=0.001). In conclusion, AI-driven CCTA analysis exhibited promising results. AI-CCTA demonstrated a moderate relationship with QCA in intermediate coronary stenosis lesions; however, its results surpassed those of manual evaluations.
期刊介绍:
Published 24 times a year, The American Journal of Cardiology® is an independent journal designed for cardiovascular disease specialists and internists with a subspecialty in cardiology throughout the world. AJC is an independent, scientific, peer-reviewed journal of original articles that focus on the practical, clinical approach to the diagnosis and treatment of cardiovascular disease. AJC has one of the fastest acceptance to publication times in Cardiology. Features report on systemic hypertension, methodology, drugs, pacing, arrhythmia, preventive cardiology, congestive heart failure, valvular heart disease, congenital heart disease, and cardiomyopathy. Also included are editorials, readers'' comments, and symposia.