AI-Driven Quantitative Coronary CT Angiography in Suspected Coronary Artery Disease

doi:10.1016/j.jacadv.2026.102618

AI-Driven Quantitative Coronary CT Angiography in Suspected Coronary Artery Disease

JACC AdvPublication date not resolved

multicenter, international, observational cohort study
registry

Authors' conclusion

This first multicenter global registry with AI-guided quantitative CT identified noncalcified plaque burden and increment in stenosis severity as the most powerful predictors of MACE, demonstrating the interplay between traditional and novel measures of the severity of CAD. Standardized and rapid quantitative assessment of CAD may improve clinical implementation of multidimensional assessment of CAD as a cornerstone for risk assessment.

Study context

Population: Patients with clinically indicated CCTA and follow-up for major adverse cardiovascular events (MACE); patients without cardiac symptoms and prior coronary artery disease (CAD) excludedage 58.8 ± 12.5 years, 50.5% male, median follow-up 4.27 (IQR: 3.47–5.08) years
Participants: 3,551 patients

Exposure: prognostic value of AI-QCT for MACE against the risk factor-weighted clinical likelihood model

Main finding

As reported in the abstract

Primary MACE endpoint: composite of all-cause death, myocardial infarction (MI), stroke, congestive heart failure, late revascularizations, and hospitalization for unstable angina · median 4.27 (IQR: 3.47-5.08) years

independent predictor after excluding collinear variables

Estimate (95% CI)

diameter stenosis

HR 1.25 per 10% increase(1.18–1.32)

noncalcified plaque volume

HR 1.07 per 50 mm3 increase(1.03–1.11)

Each row per its own increment, as printed

Events: 167 (4.7%)
AUC, risk factor-weighted clinical likelihood model: 0.63 (95% CI: 0.58-0.67)
AUC, adding AI-QCT-based diameter stenosis and noncalcified plaque volume: 0.76 (95% CI: 0.77-0.80), P < 0.001

Observational data: associations, not effects.

Further findings

Discrimination: AI-QCT added to traditional risk factors, age, and sex

AUC, traditional risk factors, age, and sex: 0.67
AUC, adding AI-QCT: 0.77; P < 0.001

Discrimination: AI-QCT vs atherosclerotic cardiovascular disease risk score

AUC, atherosclerotic cardiovascular disease risk score: 0.63; 95% CI: 0.58-0.68
AUC, with AI-QCT: 0.75 (95% CI: 0.69-0.80; P < 0.001)

Secondary MACE endpoint: all-cause death and myocardial infarction (MI)

Similar results were seen for the secondary MACE endpoint of death/MI

Original publication

AI-Driven Quantitative Coronary CT Angiography in Suspected Coronary Artery Disease. JACC Adv. doi:10.1016/j.jacadv.2026.102618

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Summarised from the published abstract; every number is checked against it