Validation of AI-enhanced ECG image analysis for identifying extreme cardiac magnetic resonance metrics in a cross-ethnic UK biobank study

Artificial intelligence enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, but validation against cardiac magnetic resonance (CMR), the reference standard for cardiac structure and function, remains limited. We evaluated the diagnostic performance of six AI-ECG models in identifying CMR-defined cardiac abnormalities in a population-based cohort. This cohort study included 38,804 UK Biobank participants with paired 12-lead ECG and CMR data. AI-ECG models, originally developed using Korean hospital-based datasets, were externally validated in this sample. Cardiac abnormalities were defined as the top 1% of CMR-derived values. Outcomes were the performance of AI-ECG in detecting left and right ventricular dysfunction, based on ejection fraction (QCG-LVD and QCG-RVD) and strain (ECG-LVGLS and ECG-RVGLS), and structural abnormalities including left ventricular hypertrophy (LVH) and left atrial enlargement (LAE). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with subgroup analyses by age, sex, and comorbidities. Among 38,804 participants (mean age 64.2 years; 48.2% male), AI-ECG models showed high accuracy for detecting left ventricular dysfunction (AUC 0.887 for QCG-LVD and 0.896 for ECG-LVGLS) and right ventricular dysfunction (AUC 0.778 for QCG-RVD and 0.825 for ECG-RVGLS). In a sub-cohort of 21,267 individuals, AUCs were 0.824 for detecting LVH and 0.883 for LAE. Subgroup analyses showed consistent performance, with higher accuracy among older individuals, males, and those with hypertension or ischemic heart disease. In this large multiethnic cohort, AI-ECG models demonstrated strong performance in detecting CMR-defined abnormalities, supporting their potential as a scalable, noninvasive screening tool for cardiovascular disease.