Why the study?
Hypertrophic cardiomyopathy remains widely underdiagnosed in pediatric populations, and the ECGVision HCM model had only been validated in adults.
Does the ECGVision HCM© model accurately identify hypertrophic cardiomyopathy in a high-risk adolescent population?
Does the ECGVision HCM© model accurately identify hypertrophic cardiomyopathy in a high-risk adolescent population?
The ECGVision HCM© artificial intelligence algorithm demonstrates excellent sensitivity and specificity for identifying hypertrophic cardiomyopathy in a high-risk adolescent population.
Supports AI-ECG HCM screening in at-risk adolescents; extends adult validation to pediatrics and warrants larger trials.
Hypertrophic cardiomyopathy (HCM) remains widely underdiagnosed in both adult and pediatric population. The ECGVision HCM© model leverages artificial intelligence deep learning to identify HCM and has been validated in an adult population. We aimed to assess the efficacy of this tool in identifying HCM in a high-risk adolescent population. In our 119 patients (average age 14.2 years), the model had 100% sensitivity, 96.51% specificity, a positive predictive value of 66.67%, and a negative predictive value of 100%. Our study demonstrates the efficacy of the ECGVision HCM© algorithm in an adolescent population and suggests that it may serve as a valuable screening tool.
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Bavishi et al. (2026) studied this question.
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