Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

Abstract

In this study, the AI-ECG was found to distinguish patients with electrocardiographically concealed LQTS from those discharged without a diagnosis of LQTS and provide a nearly 80% accurate pregenetic test anticipation of LQTS genotype status. This model may aid in the detection of LQTS in patients presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in the general population.