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Published on: December 11, 2019
Utility of artificial intelligence electrocardiogram screening tool for hypertrophic cardiomyopathy in an adolescent
Aakash Bavishi1, Atreya Mishra2, Andrew La Valle3
1Department of Cardiology, University of Illinois, Chicago, IL, USA.
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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