AI aided CT angiography promising for CAD risk stratification, finds study

Atherosclerosis evaluation by coronary computed tomography angiography (CCTA) is promising for coronary artery disease (CAD) risk stratification, but time consuming and requires high expertise. Artificial Intelligence (AI) applied to CCTA for comprehensive CAD assessment may overcome these limitations. Researchers hypothesized AI aided analysis allows for rapid, accurate evaluation of vessel morphology and stenosis.

This was a multi-site study of 232 patients undergoing CCTA. Studies were analyzed by FDA-cleared software service that performs AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification and characterization with comparison to ground truth of consensus by three L3 readers. CCTAs were analyzed for: % maximal diameter stenosis, plaque volume and composition, presence of high-risk plaque and Coronary Artery Disease Reporting & Data System (CAD-RADS) category.

Results highlighted the following facts.

• AI performance was excellent for accuracy, sensitivity, specificity, positive predictive value and negative predictive value as follows: >70% stenosis: 99.7%, 90.9%, 99.8%, 93.3%, 99.9%, respectively; >50% stenosis: 94.8%, 80.0%, 97.0, 80.0%, 97.0%, respectively.
• Bland-Altman plots depict agreement between expert reader and AI determined maximal diameter stenosis for per-vessel (mean difference −0.8%; 95% CI 13.8% to −15.3%) and per-patient (mean difference −2.3%; 95% CI 15.8% to −20.4%).
• L3 and AI agreed within one CAD-RADS category in 228/232 (98.3%) exams per-patient and 923/924 (99.9%) vessels on a per-vessel basis.
• There was a wide range of atherosclerosis in the coronary artery territories assessed by AI when stratified by CAD-RADS distribution.

"Use of this FDA-cleared device as a clinical decision support tool in combination with enhanced CCTA education may improve the reproducibility of CCTA interpretation in various clinical and investigational settings," the authors wrote. "The results of this study provide an important foundational platform for future research in AI-guided atherosclerosis evaluation across a wide spectrum of diseases."they concluded.

For full article, follow the link: DOI:https://doi.org/10.1016/j.jcct.2021.05.004

Source: Journal of Cardiovascular Computed Tomography