AI-ECG Accessory Pathway Localisation Study

Not yet recruiting

• Conditions: Accessory Pathway; Artifical Intelligence; ECG

• Registration Number: NCT07083791
• Lead Sponsor: Imperial College London

Brief Summary

This study seeks to validate the real-world accuracy of an AI-based algorithm for identifying the location of an accessory pathway from the 12-lead electrocardiogram

Detailed Description

Silent validation study of an AI-ECG (artificial intelligence applied to electrocardiography) accessory pathway localisation algorithm, applied to prospective and consecutive cases in clinical practice, to determine its true accuracy and performance.

A pre-existing AI-ECG algorithm will be applied to participant ECG data, collected at the time of their clinical electrophysiology study (EPS) for ablation of their accessory pathway. This will be compared to the ground truth of the successful ablation location, determined by fluoroscopy and/or 3D electroanatomical mapping from their procedure.

Study Timeline

• Status Verified: 2025-07
• Study First Submitted: 2025-07-09
• Study First Submitted QC: 2025-07-21
• Last Update Submitted: 2025-07-21
• Study First Posted: 2025-07-24
• Last Update Posted: 2025-07-24
• Study Start: 2025-08-01
• Primary Completion: 2027-03-01
• Study Completion: 2027-03-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 100

Inclusion Criteria

• Referred for EPS procedure as part of their clinical care, with a finding of pre-excitation on their ECG
• Manifest pre-excitation on their ECG any time prior to their procedure
• Able to give consent
• Minimum age 13 years old
• Maximum age 100 years old

Exclusion Criteria

• Unable to give consent
• Adults > 100 years old
• Children < 13 years old
• Patients with known location of their accessory pathway from a previous EP study

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Performance and accuracy of the AI-ECG accessory pathway localisation algorithm	At completion of recruitment, anticipated at 18 months	Performance metrics of the AI-ECG accessory pathway localisation algorithm, including accuracy, F1-score, sensitivity, specificity, positive and negative predictive values. Benchmarked against the ground truth of human operator assessment from fluoroscopy and/or 3D electroanatomical mapping.

Secondary Outcome Measures

Name	Time	Method
Accuracy of the ground truth locations from the human operator compared to the successful ablation location	At completion of recruitment, anticipated at 18 months	The ground truth of successful ablation location determined by operator assessment of fluoroscopy ± 3D mapping will be compared to the true ablation location on a complete 3D electroanatomical annular map
Relative performance of the AI-ECG algorithm compared to human estimation	At completion of recruitment, anticipated at 18 months	Difference in performance/accuracy between the AI-ECG accessory pathway localisation algorithm and human estimation from the 12-lead ECG
Relative performance of the AI-ECG algorithm compared to manual localisation algorithms	At completion of recruitment, anticipated at 18 months	Difference in performance/accuracy between the AI algorithm and pre-specified, established manual localisation algorithms (Arruda, Milstein, Pambrun, Boersma, D'Avila and Chiang)

Trial Locations

Locations (1)

Imperial College Healthcare NHS Trust; 🇬🇧 London, United Kingdom