Revolucionando o Diagnóstico Cardíaco: A Eficácia da Inteligência Artificial na Interpretação de Eletrocardiogramas

Authors

  • Raíssa Corrêa Torres Faculdade São Leopoldo Mandic (FSLMandic)
  • Victor Gabriel Moreira Viana Universidade Federal do Paraná (UFPR)
  • Larissa Albuquerque Oliveira Centro Universitário Christus (UNICHRISTUS)
  • Bárbara Souto Villela Universidade Federal de Juiz de Fora (UFJF)
  • Régia Freitas Universidade de Volta Redonda (UniFOA)
  • Sâmia Quirino da Silva Centro Universitário Christus (UNICHRISTUS)
  • Luann Joviniano Chagas Universidade Vila Velha (UVV)
  • Júlia Silva Costa Afya Faculdade de Ciências Médicas de Ipatinga
  • Matheus de Oliveira Perobelli Faculdade de Ciências Médicas e da Saude de Juiz de Fora (SUPREMA)
  • Evelyn Odete Quintão Zacarias Siqueira Afya Faculdade de Ciências Médicas de Ipatinga (AFCMI)
  • Luciano Cortes Drubi Centro Universitário de Belo Horizonte (UNIBH)
  • Lucas do Couto Tonholo Faculdade de Medicina de Barbacena (FAME/FUNJOBE)

DOI:

https://doi.org/10.36557/2674-8169.2024v6n8p1588-1595

Keywords:

Artificial Intelligence; Electrocardiogram; Cardiac Diagnosis.

Abstract

Artificial intelligence (AI) has been revolutionizing cardiology, particularly through its integration with electrocardiograms (ECGs). This study aims to evaluate the effectiveness of AI in interpreting ECGs for the diagnosis of heart diseases. The narrative literature review encompasses articles published between 2020 and 2024, focusing on research applying AI and machine learning (ML) to ECG analysis. The results show that AI can transform ECGs into an effective screening and prediction tool, identifying subclinical patterns that are often imperceptible. They highlight the need for AI/ML literacy for effective clinical implementation. They reinforce AI's potential to enhance ECGs, transforming them into a powerful biomarker, and note that AI-assisted analysis can overcome the limitations of classical methods, expanding ECG functionality. Although AI in ECG presents challenges related to validation, data privacy, and algorithm comprehension, it continues to promise significant improvements in the early detection and preventive intervention of heart diseases.

Downloads

Download data is not yet available.

References

ATTIA, Z. I.; HARMON, D. M.; BEHR, E. R.; FRIEDMAN, P. A. Application of artificial intelligence to the electrocardiogram. Eur Heart J., v. 42, n. 46, p. 4717-4730, 2021.

NAGARAJAN, V. D.; LEE, S. L.; ROBERTUS, J. L.; NIENABER, C. A.; TRAYANOVA, N. A.; ERNST, S. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J., v. 42, n. 38, p. 3904-3916, 2021.

FEENY, A. K.; CHUNG, M. K.; MADABHUSHI, A.; et al. Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ Arrhythm Electrophysiol., v. 13, n. 8, p. e007952, 2020.

SIONTIS, K. C.; NOSEWORTHY, P. A.; ATTIA, Z. I.; FRIEDMAN, P. A. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol., v. 18, n. 7, p. 465-478, 2021.

HAVERKAMP, W.; STRODTHOFF, N.; ISRAEL, C. EKG-Diagnostik mit Hilfe künstlicher Intelligenz: aktueller Stand und zukünftige Perspektiven – Teil 2 : Aktuelle Studienlage und Ausblick. Herzschrittmacherther Elektrophysiol., v. 33, n. 3, p. 305-311, 2022.

SAFDAR, M. F.; NOWAK, R. M.; PAŁKA, P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med., v. 170, p. 107908, 2024.

MARTÍNEZ-SELLÉS, M.; MARINA-BREYSSE, M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis., v. 10, n. 4, p. 175, 2023.

KASHOU, A. H.; MAY, A. M.; NOSEWORTHY, P. A. Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology. Curr Cardiol Rep., v. 22, n. 8, p. 57., 2020.

Downloads

Published

2024-08-22

How to Cite

Corrêa Torres, R., Moreira Viana, V. G., Albuquerque Oliveira, L., Souto Villela , B., Freitas, R., Quirino da Silva, S., Joviniano Chagas, L., Silva Costa, J., de Oliveira Perobelli, M., Quintão Zacarias Siqueira, E. O., Cortes Drubi, L., & do Couto Tonholo, L. (2024). Revolucionando o Diagnóstico Cardíaco: A Eficácia da Inteligência Artificial na Interpretação de Eletrocardiogramas. Brazilian Journal of Implantology and Health Sciences, 6(8), 1588–1595. https://doi.org/10.36557/2674-8169.2024v6n8p1588-1595

Issue

Vol. 6 No. 8 (2024): BJIHS QUALIS B3 - FATOR DE IMPACTO SJIF 5.807

Section

Systematic Review

License

Copyright (c) 2024 Raíssa Corrêa Torres, Victor Gabriel Moreira Viana, Larissa Albuquerque Oliveira, Bárbara Souto Villela , Régia Freitas, Sâmia Quirino da Silva, Luann Joviniano Chagas, Júlia Silva Costa, Matheus de Oliveira Perobelli, Evelyn Odete Quintão Zacarias Siqueira, Luciano Cortes Drubi, Lucas do Couto Tonholo

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors are copyright holders under a CCBY 4.0 license.