Artificial intelligence (AI)-based coronary calcium quantification successfully identified patients who could benefit from lipid-lowering therapy

1. Hagopian and colleagues developed a deep-learning model to quantify coronary artery calcium on computed tomography scans across Veterans Affairs hospitals.

2. AI-derived calcium scores predicted cardiovascular outcomes and identified screening patients for lipid-lowering therapy.

Evidence Rating Level: 2 (Good)

Study Rundown: Coronary artery calcium (CAC) quantification using electrocardiogram (ECG)-gated computed tomography (CT) scans is well-established for determining cardiovascular risk. However, CAC is not routinely quantified on non-gated CT scans obtained for noncardiac reasons. Hagopian and colleagues developed AI-CAC, a deep-learning model that automatically segments and scores CAC on non-contrast, non-gated chest CTs. The model was developed with expert segmentations and was tested against clinical electrocardiogram (ECG)-gated CAC scores in patients with paired scans. Main outcomes included threshold classification accuracy and agreement with reference scores. In 795 paired scans, AI-CAC achieved 89.4% accuracy for distinguishing zero from non-zero CAC and 87.3% accuracy for differentiating scores below or above 100. AI-CAC also demonstrated good agreement (kappa=0.72) with ECG-gated CAC. Additionally, four cardiologists qualitatively reviewed a random sample of patients with AI-CAC scores greater than 400 and confirmed that 99.2% would benefit from lipid-lowering therapy. This study demonstrated the potential for using AI and CT scans to screen for CAC on a larger scale.