Real-World Validation of the AI-Enabled Vectorcardiographic CSG-Index for Obstructive Coronary Artery Disease: The VECTOR-CAD Study

Background/Objectives: Cardisiography is an artificial intelligence-enabled resting vectorcardiography (AI-VCG) system, but evidence for its accuracy in coronary artery disease (CAD) remains limited. We evaluated the AI-VCG-derived CSG-index for obstructive CAD detection, association with stenosis severity, incremental discrimination beyond clinical variables, and concordance with exercise electrocardiography (ECG).

Methods

In this two-centre retrospective diagnostic-accuracy study, 721 Cardisiography records from January 2023–December 2025 were screened and 224 patients were included. Invasive coronary angiography (ICA) was the primary reference (n = 172); coronary computed tomography angiography (CCTA) contributed only reference-negative cases (n = 52) to the hierarchical cohort. Obstructive CAD was defined as ≥50% stenosis and CSG-index ≥ −0.27 as positive. Primary analysis assessed obstructive CAD discrimination in the ICA cohort; secondary analyses assessed severe CAD (≥70%), hierarchical-cohort performance, stenosis severity, incremental value beyond available clinical variables and exercise ECG concordance.