Artificial Intelligence Standardizes Pericoronary Fat Attenuation Index Measurement: External Multi-Vendor CT Validation.

OBJECTIVES: To evaluate the feasibility and generalizability of an AI-based automated method for measuring the pericoronary fat attenuation index (FAI)-a promising non-invasive imaging biomarker for coronary inflammation-and to promote efficient clinical translation. METHODS: A retrospective analysis was conducted on coronary computed tomography angiography data from 1,125 patients (584 [51.9%] male; mean age 61.5 ± 12.3 years; totaling 3,375 coronary arteries) examined using five mainstream CT scanners from different manufacturers, models, and generations. FAI values around the major coronary arteries were quantitatively evaluated using both AI software and workstation. Agreement between methods, diagnostic performance, and scanner influence were assessed. RESULTS: Artificial intelligence-based fat attenuation index (AI-FAI) demonstrated a favorable correlation with workstation-FAI at the patient level (ρ = 0.877, p < 0.001; n = 1,125), along with a high level of agreement (mean bias: 1.60 HU; 95% limits of agreement: -4.94 to 8.14; 94.84% within limits). Using a threshold of -70.1 HU, AI-FAI demonstrated performance comparable to workstation-FAI, with accuracy (84.98%) and solid diagnostic performance (AUC = 0.851). Furthermore, AI-FAI exhibited stable measurement performance across all five scanners, with inter-scanner ρ ranging from 0.829 to 0.896 and only 3.70-6.06% of outliers beyond the agreement limits. CONCLUSIONS: AI-FAI demonstrates favorable agreement with traditional workstation-FAI, solid diagnostic performance, and consistent cross-scanner robustness, providing a reliable automated tool for cardiovascular risk assessment. ADVANCES IN KNOWLEDGE: This is a novel study demonstrating AI-FAI as a practical alternative to workstation-FAI across multiple CT scanners, facilitating reliable quantification of coronary inflammation for routine clinical practice.