Validation of an automated artificial intelligence system for 12‑lead ECG interpretation.

Autor: Herman R; Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy; Cardiovascular Centre Aalst, Aalst, Belgium; Powerful Medical, Bratislava, Slovakia. Electronic address: robi.herman@gmail.com., Demolder A; Powerful Medical, Bratislava, Slovakia., Vavrik B; Powerful Medical, Bratislava, Slovakia., Martonak M; Powerful Medical, Bratislava, Slovakia., Boza V; Powerful Medical, Bratislava, Slovakia; Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia., Kresnakova V; Powerful Medical, Bratislava, Slovakia; Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Kosice, Slovakia., Iring A; Powerful Medical, Bratislava, Slovakia., Palus T; Powerful Medical, Bratislava, Slovakia., Bahyl J; Powerful Medical, Bratislava, Slovakia., Nelis O; Cardiovascular Centre Aalst, Aalst, Belgium., Beles M; Cardiovascular Centre Aalst, Aalst, Belgium., Fabbricatore D; Cardiovascular Centre Aalst, Aalst, Belgium., Perl L; Department of Cardiology, Rabin Medical Center, Petah Tikvah, Israel., Bartunek J; Cardiovascular Centre Aalst, Aalst, Belgium., Hatala R; Department of Arrhythmia and Pacing, National Institute of Cardiovascular Diseases, Bratislava, Slovakia. Electronic address: hatala@nusch.sk.
Jazyk: angličtina
Zdroj: Journal of electrocardiology [J Electrocardiol] 2024 Jan-Feb; Vol. 82, pp. 147-154. Date of Electronic Publication: 2023 Dec 23.
DOI: 10.1016/j.jelectrocard.2023.12.009
Abstrakt: Background: The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE.
Methods: An AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12‑lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score.
Results: A total of 932,711 standard 12‑lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses.
Conclusions: Our results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12‑lead ECG, highlighting its potential as a clinical tool for healthcare professionals.
Competing Interests: Declaration of Competing Interest Dr. Herman is the Co-founder and Chief Medical Officer of Powerful Medical and supported by a research grant from the CardioPaTh PhD Program. Michal Martonak, Jakub Bahyl, Andrej Iring, Vladimir Boza and Anthony Demolder are employees of Powerful Medical. Other authors report no conflict of interest.
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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