Ko et al. investigate the use of convolutional neural networks in accurately detecting hypertrophic cardiomyopathy based on electrocardiography readings. A convolutional neural network was trained and validated suing ECG data from 2,448 patients that had hypertrophic cardiomyopathy and 51,153 matched controls. The network was then tested on a separate dataset of 612 patients and 12,788 controls. The neural network achieved excellent performance, with an AUC of 0.96 and a specificity of 90% in the testing dataset. Analysis showed consistently high accuracy among patients with normal ECGs as well, and the algorithm performed well in younger patients, achieving a specificity of 92%. Researchers highlight how AI-driven ECG analysis could enable earlier and more accurate detection of hypertrophic cardiomyopathy, especially in populations that are harder to diagnose with traditional methods. The researchers noted that the convolutional neural network could benefit from refinement and external validation before adopting the model into clinical practices.