Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Abstract

Abstract Aim Recently, deep-learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and Results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-center North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of HCM patients and non-HCM controls from 3 external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs non-HCM status from the ECG alone was examined. A total of 773 HCM patients and 3,867 non-HCM controls were included across 3 sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for HCM patients and 0.3% for controls (p < 0.001). Overall, the AI-ECG algorithm had an AUC of 0.922 (95% CI 0.910-0.934), with diagnostic accuracy 86.9%, sensitivity 82.8% and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.