February 11, 2021JAMA Cardiology132 citationsOpen Access

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

JBJ. Martijn BosElectrophysiology ZAZachi I. AttiaElectrophysiology DAD.J. AlbertGenVec

Structured PICO

Does an AI-ECG model accurately identify electrocardiographically concealed Long QT Syndrome and anticipate genotype status from a standard 12-lead ECG?

Population

Patients evaluated for Long QT Syndrome (LQTS), including those with electrocardiographically concealed LQTS and those discharged without a diagnosis of LQTS

Intervention

Artificial Intelligence and Deep Neural Networks (AI-ECG) applied to surface 12-lead electrocardiogram

Outcome

Distinction of concealed LQTS from non-LQTS and accuracy of pregenetic test anticipation of LQTS genotype statussurrogate

An AI-ECG model can accurately identify electrocardiographically concealed Long QT Syndrome and anticipate genotype status, potentially aiding early detection in arrhythmia clinics.

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.

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