Artificial Intelligence-Powered Automatic Coronary Computed Tomography Angiography Plaque Quantification: Comparison Against Optical Coherence Tomography

Abstract Background Coronary computed tomography angiography (CCTA) enables a non-invasive, comprehensive assessment of coronary artery disease, and artificial intelligence (AI) offers the potential to improve CCTA image interpretation. Aims This study aimed to evaluate the performance of an AI-powered method for automatic plaque quantification from CCTA, with optical coherence tomography (OCT) as reference standard. Methods Patients who underwent CCTA within six months prior to OCT were retrospectively enrolled. AI-assisted automatic plaque quantification was performed on CCTA with specific plaque composition classification based on adaptive Hounsfield unit thresholds. Qualitative high-risk plaque features were also assessed. Automated co-registration of CCTA and OCT was performed with the link of invasive coronary angiography. Results A total of 91 patients with 153 co-registered lesions were evaluated. The AI-assisted automatic CCTA analysis showed significant correlations with OCT for quantifying plaque volume/burden and different plaque compositions (all P values 0.001); of which, the correlation coefficient for plaque volume was 0.84. Vulnerable plaque, defined as lipid-to-cap ratio 0.33 on OCT, was identified in 39 (25.5%) lesions. CCTA-derived plaque volume 82.5 mm3 (odds ratio OR, 9.39), maximal plaque burden 76.4% (OR, 3.70), lipidic tissue volume 16.3 mm³ (OR, 4.42), all P 0.001, and high-risk plaque features ≥2 (OR, 2.70, P = 0.009) were independent predictors of OCT-derived vulnerable plaques. The average time for automatic CCTA plaque quantification was 1.8 minutes per patient. Conclusions The novel AI-powered method facilitated fully automatic plaque quantification and correlated well with co-registered OCT.