Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction.

Autor: Lee SH; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Jeon KL; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea., Lee YJ; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., You SC; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea. Electronic address: chandryou@yuhs.ac., Lee SJ; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Hong SJ; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Ahn CM; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Kim JS; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Kim BK; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Ko YG; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Choi D; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea., Hong MK; Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Jazyk: angličtina
Zdroj: Annals of emergency medicine [Ann Emerg Med] 2024 Nov; Vol. 84 (5), pp. 540-548. Date of Electronic Publication: 2024 Jul 26.
DOI: 10.1016/j.annemergmed.2024.06.004
Abstrakt: 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.
(Copyright © 2024 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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