ArticleAnnals of pediatric cardiology
Utility of artificial intelligence electrocardiogram screening tool for hypertrophic cardiomyopathy in an adolescent population.
Article in Annals of pediatric cardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
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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