ROMIAE(利用人工智能心电图分析排除急性心肌梗死)试验研究方案:一项前瞻性多中心观察研究,旨在验证基于深度学习的12导联心电图分析模型用于检测急诊科患者的急性心肌梗死。

IF 1.9 Q2 EMERGENCY MEDICINE Clinical and Experimental Emergency Medicine Pub Date : 2023-12-01 Epub Date: 2023-11-28 DOI:10.15441/ceem.22.360
Tae Gun Shin, Youngjoo Lee, Kyuseok Kim, Min Sung Lee, Joon-Myoung Kwon
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引用次数: 0

摘要

目的:基于人工智能(AI)的发展,一些新兴的方法在利用心电图(ECG)诊断急性心肌梗死(AMI)方面取得了突出的表现。然而,使用多中心前瞻性设计检测AMI的AI-ECG分析尚未进行。本前瞻性多中心观察性研究旨在验证AI-ECG模型在急诊科患者中检测AMI的效果。方法:将在韩国18个急诊医疗中心招募约9,000名患有胸痛和/或类似AMI症状的成年患者。我们开发并验证的AI-ECG分析算法将用于本研究。主要终点是到达急救中心当天的AMI诊断,次要终点是30天内的主要心脏不良事件。从2022年3月起,患者登记开始在机构审查委员会批准的中心进行。讨论:这是第一项前瞻性研究,旨在确定基于人工智能的12导联心电图分析算法在多中心急诊科诊断AMI的有效性。本研究可能为深度学习在急诊科心电图检测AMI中的应用提供见解。试验注册:ClinicalTrials.gov标识符:NCT05435391。注册于2022年6月28日。
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ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department.

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.

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CiteScore
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自引率
10.50%
发文量
59
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