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Article

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

JAMA cardiology - 1 Apr 2024

Jiang River, Cheung Christopher C, Garcia-Montero Marta, Davies Brianna, Cao Jason, Redfearn Damian, Laksman Zachary M, Grondin Steffany, Atallah Joseph, Escudero Carolina A, Cadrin-Tourigny Julia, Sanatani Shubhayan, Steinberg Christian, Joza Jacqueline, Avram Robert, Tadros Rafik, Krahn Andrew D

Abstract excerpt

Importance: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). Objective: To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and...

Read the complete abstract on PubMed

Topics

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Share to Arrhythmias, Cardiac
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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Share to KCNH2 gene

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Source

Open in PubMed Open full text

Identifiers

PMID: 38446445
DOI: 10.1001/jamacardio.2024.0039
PMCID: PMC10918571

Genes

KCNH2
KCNQ1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

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