| Autor: |
Tae Gun Shin, Youngjoo Lee, Kyuseok Kim, Min Sung Lee, Joon-myoung Kwon, on behalf of the ROMIAE study group |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Clinical and Experimental Emergency Medicine, Vol 10, Iss 4, Pp 438-445 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2383-4625 |
| DOI: |
10.15441/ceem.22.360 |
| Popis: |
Objective Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. Methods Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. Discussion This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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