AI-ECG for LVSD detection: a systematic review and first-in-kind multinational head-to-head comparison
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Background and aims
Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfunction (LVSD), but their head-to-head agreement and performance have not been independently evaluated within the same cohort. This study aimed to systematically characterize available AI-ECG models for LVSD detection, and for the first time evaluate their head-to head performance.
Methods
We systematically reviewed published AI-ECG models for LVSD detection, extracting data on architecture, cohort characteristics, and performance. Authors of these studies were invited to share models for external validation in a deeply phenotyped registry of all patients undergoing cardiac magnetic resonance imaging (CMR) in the Amsterdam UMC with paired ECGs. Performance was assessed in all consecutive patients and in a subset reflecting a lower-complexity sample (15% prevalence of LVEF <40%).
Results
We identified 35 studies describing 51 AI-ECG models, all showing high performance (area under the receiver operating characteristic curve [AUROC]>80) or excellent (AUROC >90) performance. Four research groups (Korea, the United States, Taiwan, The Netherlands) shared models for external validation. AUROCs ranged from 0.83–0.93 in all consecutive patients (n = 1,306; mean age 59 ± 15 years; 450 [35%] female) and 0.87–0.96 in the lower complexity subset. Performance remained consistent across subgroups, with minor degradations in ECGs displaying a wide QRS-complex or atrial fibrillation.
Conclusions
In this first-in-kind independent validation and head-to-head comparison study, AI-ECG for LVSD detection exhibited robust performance despite disparate training populations. However, limited availability of models impedes independent validation and clinical adoption.
