Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study.

BACKGROUND

Accurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows.

METHODS

This multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC).

RESULTS

For ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06.

CONCLUSIONS

The AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.