AI Opportunistic Coronary Calcium Screening at Veterans Affairs Hospitals.

BACKGROUND

Coronary artery calcium (CAC) is highly predictive of cardiovascular events. Although millions of chest computed tomography (CT) scans are performed annually in the United States, CAC is not routinely quantified from scans done for noncardiac purposes.

METHODS

We developed a deep learning algorithm, AI-CAC, using 446 expert segmentations to automatically quantify CAC on noncontrast, nongated CT scans. Our study differs from prior works by utilizing imaging data from 98 medical centers across the Veterans Affairs national health care system, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on nongated scans was compared against clinical standard electrocardiogram (ECG)-gated CAC scoring in 795 patients with paired gated scans within 1 year of their nongated scan. In addition, the model was tested on 8052 low-dose CTs (LDCTs) to simulate opportunistic CAC screening.

RESULTS

Nongated AI-CAC differentiated zero versus nonzero and less than 100 versus 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively. Nongated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox hazard ratio 3.49; P<0.005), and composite first-time stroke, myocardial infarction, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox hazard ratio 3.00; P<0.005). In the LDCT dataset, 3091 out of 8052 (38.4%) individuals had AI-CAC scores >400. Four cardiologists qualitatively reviewed a random sample of the >400 AI-CAC LDCT patients and verified that 527 of the 531 (99.2%) would benefit from lipid-lowering therapy.

CONCLUSIONS

This nongated CT CAC algorithm was developed across a national health care system and shows strong performance in evaluation against paired gated CT scans. The model code and weights are available at https://github.com/Raffi-Hagopian/AI-CAC/. (Funded by the Veterans Affairs health care system.).