Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment

Aims: Proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected on gated cardiac computer tomography (CT) and whether it provides prognostic significance with artificial intelligence (AI).

Methods and results: A total of 2016 asymptomatic adults with baseline CAC CT scans from a single site were followed up for MACE for 14 years. An AI algorithm to classify CAC into proximal or not was created using expert annotations of total and proximal CAC and AI-derived cardiac structures. The algorithm was evaluated for prognostic significance on AI-derived CAC segmentation. In 303 subjects with expert annotations, the classification of proximal versus not proximal CAC reached an area under receiver operating curve of 0.93 (95% confidence interval [CI] 0.91-0.95). For prognostic evaluation, in an additional 588 subjects with mild AI-derived CAC scores, the AI proximal involvement was associated with worse MACE-free survival (P=0.008) and higher risk of MACE when adjusting for CAC score alone (hazard ratio [HR] 2.28, 95% CI 1.16-4.48, P=0.02) or CAC score and clinical risk factors (HR 2.12, 95% CI 1.03-4.36, P=0.04).

Conclusion: The AI algorithm could identify proximal CAC on CAC CT. The proximal location had modest prognostic significance in subjects with mild CAC scores. The AI identification of proximal CAC can be integrated into automatic CAC scoring and improves the risk prediction of CAC CT.