AI-Based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST) (EP-AI ASSIST)
AI-based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST)
This multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. By utilizing encrypted raw 12-lead ECG text data and deep neural network models, the study evaluates the effectiveness of AI-enhanced ECG in detecting cardiac abnormalities and predicting clinical outcomes.
研究概览
地位
研究类型
注册 (估计的)
联系人和位置
学习联系方式
- 姓名：Yongsoo Baek, MD, PhD
- 电话号码：+82-32-890-2200
- 邮箱：existsoo@inha.ac.kr
学习地点
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Incheon
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Incheon、Incheon、韩国、22332
- 招聘中
- Inha University Hospital
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接触：
- Yongsoo Baek, MD, PhD
- 电话号码：+82-32-890-2200
- 邮箱：existsoo@inha.ac.kr
-
-
参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
取样方法
研究人群
Retrospective cohort of patients aged 19 years or older with PSVT, WPW syndrome, PVC, or VT who underwent electrophysiological study and catheter ablation at participating institutions between January 2005 and December 2023.
描述
Inclusion Criteria:
- Age 19 years or older
- Diagnosis of one of the following arrhythmias: paroxysmal supraventricular tachycardia (PSVT), Wolff-Parkinson-White (WPW) syndrome, premature ventricular contractions (PVC), or ventricular tachycardia (VT)
- Underwent an electrophysiological study (EPS) and catheter ablation
- Available 12-lead electrocardiogram (ECG) records that can be extracted as text (XML) files
Exclusion Criteria:
- Inadequate ECG data quality, as judged by the investigator
- Missing medical records required for the study, as judged by the investigator
学习计划
研究是如何设计的？
设计细节
- 观测模型：COHORT
- 时间观点：RETROSPECTIVE
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
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Cardiovascular Disease and Tachycardia Cohort
Patients aged 19 years or older diagnosed with tachycardias (including PSVT, WPW syndrome, PVC, or VT) who underwent an electrophysiological study (EPS) and radiofrequency catheter ablation (RFCA), with extractable 12-lead ECG text data[cite: 1]. |
Application of a deep learning-based 12-lead electrocardiogram (ECG) algorithm to analyze raw text data for the differential diagnosis and localization of tachycardias, including PSVT, WPW syndrome, PVC, and VT[cite: 1]. |
研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Area Under the ROC Curve (AUC) of AI-ECG Models for Differentiating Tachycardia Mechanisms Confirmed by Electrophysiological Study
大体时间：Baseline
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Diagnostic performance of Transformer-based deep learning models applied to 12-lead ECG raw data for differentiating tachycardia mechanisms, including atrioventricular nodal reentrant tachycardia (AVNRT) versus atrioventricular reentrant tachycardia (AVRT). The reference standard is the diagnosis confirmed by electrophysiological study and catheter ablation. Performance is summarized as AUC with 95% confidence interval (range 0-1; higher values indicate better discrimination). |
Baseline
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Area Under the ROC Curve (AUC) of AI-ECG Models for Localizing Arrhythmia Origin Sites (Accessory Pathway, PVC, and VT) Confirmed by Successful Catheter Ablation
大体时间：Baseline
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Performance of AI-ECG models in classifying the anatomical site of the arrhythmia substrate from the pre-ablation 12-lead ECG, including (1) accessory pathway location in Wolff-Parkinson-White syndrome, (2) the origin of premature ventricular contractions, and (3) the origin of ventricular tachycardia. The reference standard is the site identified by electrophysiological mapping and confirmed by successful catheter ablation. For each classification task, performance is reported as AUC with 95% confidence interval (range 0-1; higher values indicate better classification). |
Baseline
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他相关的 MeSH 术语
其他研究编号
- 2023-12-033
- 2023-12-033 (注册表标识符：Inha University Hospital Institutional Review Board)
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
IPD 计划说明
Individual participant data (IPD) will not be shared with other researchers due to institutional data privacy policies and ethical regulations concerning retrospective clinical records and encrypted electrocardiogram data.
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