Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction.
Ann Emerg Med
; 2024 Jul 25.
Article
em En
| MEDLINE
| ID: mdl-39066765
ABSTRACT
STUDY OBJECTIVE:
Although the importance of primary percutaneous coronary intervention has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory activation remains suboptimal. This study aimed to develop a precise artificial intelligence (AI) model for the diagnosis of STEMI and accurate cardiac catheterization laboratory activation.METHODS:
We used electrocardiography (ECG) waveform data from a prospective percutaneous coronary intervention registry in Korea in this study. Two independent board-certified cardiologists established a criterion standard (STEMI or Not STEMI) for each ECG based on corresponding coronary angiography data. We developed a deep ensemble model by combining 5 convolutional neural networks. In addition, we performed clinical validation based on a symptom-based ECG data set, comparisons with clinical physicians, and external validation.RESULTS:
We used 18,697 ECGs for the model development data set, and 1,745 (9.3%) were STEMI. The AI model achieved an accuracy of 92.1%, sensitivity of 95.4%, and specificity of 91.8 %. The performances of the AI model were well balanced and outstanding in the clinical validation, comparison with clinical physicians, and the external validation.CONCLUSION:
The deep ensemble AI model showed a well-balanced and outstanding performance. As visualized with gradient-weighted class activation mapping, the AI model has a reasonable explainability. Further studies with prospective validation regarding clinical benefit in a real-world setting should be warranted.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Ann Emerg Med
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Coréia do Sul