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An electrocardiogram study using artificial intelligence in the identification of arrhythmia resection site

Research for Pioneering Artificial Intelligence-Enhanced Arrhythmia Ablation Site Identification Using Twelve-Lead Electrocardiogram (EP-AI ASSIST): Multicenter retrospective study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0009317
Enrollment
1500
Registered
2024-04-08
Start date
2024-04-01
Completion date
Unknown
Last updated
2024-04-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Inha University Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Arrhythmia patients with electrocardiogram records, electrophysiological tests, and arrhythmia resection records in INHA university hospital. 1)paroxysmal supraventricular tachycardia(PSVT): Normal sinus rhythm and tachycardia in patients with atrioventricular nodular regression tachycardia and atrioventricular regression tachycardia by electrophysiological examination and arrhythmia resection 2)Wolf-Parkinson-White Syndrome (WPW): WPW electrocardiogram in patients with proven parenteral insufficiency by electrophysiological examination and arrhythmia resection 3)Ventricular premature contraction (PVC): Electrical physiology and arrhythmia resection with ventricular premature contraction (PVC) including early contraction in patients with proven location of ventricular premature contraction 4)Ventricular tachycardia (VT): An electrophysiological examination and arrhythmia resection were performed with ventricular tachycardia (VT) to prove the location of ventricular tachycardia

Exclusion criteria

Exclusion criteria: The above study is a data collection study using electrocardiogram, and there are no exclusion criteria other than patients who are unsuitable as subjects according to the researcher's judgment

Design outcomes

Primary

MeasureTime frame
Development of an electrocardiogram prediction program using a 12-lead electrocardiogram artificial intelligence algorithm by encrypting and extracting raw data from cardiovascular disease patients

Secondary

MeasureTime frame
(Developed AI-based electrocardiogram prediction program) Interpreting 12-lead ECG using AI provides important clues to the preparation and execution of resection procedures for WPW syndrome, PVC and VT, helping EP professionals in safety and efficiency

Countries

Korea, Republic of

Contacts

Public ContactHyo Bin Shin

Inha University Hospital

hbshin882@naver.com+82-32-890-2461

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Feb 4, 2026