Key result
AI-enabled vectorcardiography identifies obstructive CAD with an AUC of ~0.82.
Why the study?
Evidence for the accuracy of the AI-enabled vectorcardiography Cardisiography system in detecting coronary artery disease remains limited.
Does the AI-VCG-derived CSG-index accurately detect obstructive coronary artery disease in patients undergoing coronary angiography?
Observational (n=224)
Yes
Does the AI-VCG-derived CSG-index accurately detect obstructive coronary artery disease in patients undergoing coronary angiography?
Effect estimate: AUC 0.820
The AI-enabled resting vectorcardiographic CSG-index demonstrates good diagnostic accuracy for obstructive CAD, adding discriminatory value beyond routine clinical variables.
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. Results: Obstructive CAD was present in 76/172 patients. In the ICA cohort, CSG-index area under the curve (AUC) was 0.820, with 80.3% sensitivity and 78.1% specificity; AUCs were 0.843 in the hierarchical cohort and 0.785 for severe CAD. CSG-index increased across stenosis classes. After adjustment for available clinical variables, it remained independently associated with obstructive CAD and increased model AUC from 0.806 to 0.886. In the exploratory exercise ECG subgroup (n = 83), agreement was 66.3%, with fewer false-positive CSG-index classifications. Conclusions: The locked CSG-index showed balanced performance for obstructive CAD, provided graded information across stenosis classes, and added discrimination beyond available clinical variables. These findings support prospective multicentre evaluation as a complementary non-invasive resting test.
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Çekin et al. (2026) conducted an observational in Obstructive Coronary Artery Disease (n=224). AI-enabled vectorcardiographic CSG-index vs. Invasive coronary angiography or CCTA was evaluated on Obstructive CAD discrimination (≥50% stenosis) in the invasive coronary angiography cohort (AUC 0.820). The AI-enabled vectorcardiographic CSG-index discriminated obstructive coronary artery disease with an area under the curve of 0.820 (80.3% sensitivity and 78.1% specificity).
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