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Objective:  Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could benefit from the establishment of genetic probability prior to the test. This study aimed to develop a simple tool to provide genotype prediction for HCMs. Methods:  A convolutional neural network (CNN) was built with the 12-lead electrocardiogram (ECG) of 124 HCMs who underwent genetic testing (GT), externally tested by predicting the genotype on another HCMs cohort ( n  = 54), and compared with the conventional methods (the Mayo and Toronto score). Using a third cohort of HCMs ( n  = 76), the role of the network in risk stratification was explored by calculating the sudden cardiac death (SCD) risk scorers (HCM risk-SCD) across the predicted genotypes. Score-CAM was employed to provide a visual explanation of the network. Results:Overall, 80 of 178 HCMs (45%) were genotype-positive. Using the 12-lead ECG as input, the network showed an area under the curve (AUC) of 0.89 (95% CI, 0.83–0.96) on the test set, outperforming the Mayo score (0.69 [95% CI, 0.65–0.78],  p  &lt; 0.001) and the Toronto score (0.69 [95% CI, 0.64–0.75],  p  &lt; 0.001). The network classified the third cohort into two groups (predicted genotype-negative vs. predicted genotype-positive). Compared with the former, patients predicted genotype-positive had a significantly higher HCM risk-SCD (0.04 ± 0.03 vs. 0.03 ± 0.02,  p  &lt;0.01). Visualization indicated that the prediction was heavily influenced by the limb lead. Conclusions:  The network demonstrated a promising ability in genotype prediction and risk assessment in HCM.

Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study