Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic cardiomyopathy

Artificial intelligence (AI)-based electrocardiogram (ECG) analysis tools have shown promise in detecting various cardiac conditions. However, their performance in specific patient populations, such as those with hypertrophic cardiomyopathy (HCM), remains incompletely characterized. To evaluate the performance of three AI-based ECG analysis tools in patients with confirmed HCM: (1) a tool calculating HCM probability, (2) a tool calculating structural heart disease (SHD) probability, and (3) a tool providing ECG-based diagnoses across multiple categories.

We analysed digitized 12-lead ECGs from patients with confirmed HCM (n = 681) using three AI tools. We assessed the distribution of AI-calculated probabilities and their associations with clinical parameters and evaluated agreement between AI-based and manually assigned ECG diagnoses using Cohen’s kappa. Despite all patients having confirmed HCM, the AI-calculated HCM probabilities showed a relatively uniform distribution [median 38.8% (IQR: 12.8–63.4%)], with only 41.2% and 12.5% of patients receiving a probability score >50% and >75%. HCM probabilities were significantly higher in patients with abnormal vs. normal ECGs (P < 0.001) and correlated with markers of disease severity. SHD probabilities were generally higher [median 51.4% (IQR: 28.7–74.5%)], with 51.2% and 25% of patients receiving scores >50% and >75%.

AI-based ECG analysis tools demonstrated modest performance in our HCM cohort. These findings highlight the challenges of applying AI tools developed in general populations to specific disease cohorts and underscore the need for disease-specific validation before clinical implementation.