A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy.

Journal: Journal of electrocardiology

PMID: 39919503

Abstract

INTRODUCTION: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in younger individuals. Accurate diagnosis is crucial for management and improving patient outcomes. The application of convolutional Neural Networks (CNN), a type of AI modeling, to electrocardiogram (ECG) analysis, presents a promising and optimistic avenue for the detection of HCM. We conducted a meta-analysis to assess the effectiveness of CNN models in diagnosing HCM through ECG.

Authors

Ivo Queiroz

Catholic University of Pernambuco, Medicine Department, Recife, Brazil. Electronic address: Ivoqcjj@gmail.com.
Maria L R Defante

Redentor University Center, Medicine Department, Itaperuna, Brazil.
Lucas M Barbosa

Federal University of Minas Gerais, Department of Medicine, Belo Horizonte, Brazil.
Arthur Henrique Tavares

University of Pernambuco, Medicine Department, Recife, Brazil.
Túlio Pimentel

Federal University of Pernambuco, Medicine Department, Recife, Brazil.
Beatriz Ximenes Mendes

Unichristus, Medicine Department, Fortaleza, Brazil.

Keywords

Cardiomyopathy, Hypertrophic Convolutional Neural Networks Electrocardiography Humans Neural Networks, Computer Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (39919503)

A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy.

Abstract

Authors

Keywords

External Resources

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