Validation of an automated artificial intelligence system for 12lead ECG interpretation.
J Electrocardiol
; 82: 147-154, 2024.
Article
en En
| MEDLINE
| ID: mdl-38154405
ABSTRACT
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 12lead 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 12lead 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 12lead ECG, highlighting its potential as a clinical tool for healthcare professionals.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Síndrome Coronario Agudo
Límite:
Humans
Idioma:
En
Revista:
J Electrocardiol
/
J. eletrocardiol
/
Journal of electrocardiology
Año:
2024
Tipo del documento:
Article