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.
調査の概要
状態
研究の種類
入学 (推定)
連絡先と場所
研究連絡先
- 名前：Yongsoo Baek, MD, PhD
- 電話番号：+82-32-890-2200
- メール：existsoo@inha.ac.kr
研究場所
-
-
Incheon
-
Incheon、Incheon、韓国、22332
- 募集
- Inha University Hospital
-
コンタクト：
- Yongsoo Baek, MD, PhD
- 電話番号：+82-32-890-2200
- メール：existsoo@inha.ac.kr
-
-
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
サンプリング方法
調査対象母集団
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.
説明
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
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 観測モデル：COHORT
- 時間の展望：RETROSPECTIVE
コホートと介入
グループ/コホート |
介入・治療 |
|---|---|
|
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]. |
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Area Under the ROC Curve (AUC) of AI-ECG Models for Differentiating Tachycardia Mechanisms Confirmed by Electrophysiological Study
時間枠：Baseline
|
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
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
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
時間枠：Baseline
|
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
|
協力者と研究者
スポンサー
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
追加の関連 MeSH 用語
その他の研究ID番号
- 2023-12-033
- 2023-12-033 (レジストリ識別子：Inha University Hospital Institutional Review Board)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
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.
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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