AI-ECG Accessory Pathway Localisation Study

NCT07083791 | Not Yet Recruiting

• 1 other identifier: IRAS 347921
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

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

Conditions

Accessory Pathway; Artifical Intelligence; ECG

Keywords

AI-ECG; AI; ECG; Accessory pathway; Accessory pathway localisation

Outcome Measures

Primary Outcomes (1)

• Performance and accuracy of the AI-ECG accessory pathway localisation algorithm

  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.

  At completion of recruitment, anticipated at 18 months

Secondary Outcomes (3)

• Relative performance of the AI-ECG algorithm compared to human estimation

  At completion of recruitment, anticipated at 18 months

• Relative performance of the AI-ECG algorithm compared to manual localisation algorithms

  At completion of recruitment, anticipated at 18 months

• Accuracy of the ground truth locations from the human operator compared to the successful ablation location

  At completion of recruitment, anticipated at 18 months

Study Arms (1)

Patients with manifest pre-excitation

Patients with a previous ECG demonstrating manifest pre-excitation, referred for an electrophysiology study as part of their clinical care

Eligibility Criteria

• Age: 13 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients referred for an electrophysiology study +/- ablation, with evidence of previous pre-excitation on their 12-lead ECG.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Imperial College London: lead

Study Sites (1)

Imperial College Healthcare NHS Trust

London, W12 0HS, United Kingdom

Location

MeSH Terms

Conditions

Pre-Excitation Syndromes

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Cardiac Conduction System Disease

Study Officials

• Ahran Arnold, PhD

  Imperial College London

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Keenan Saleh, MBBS

CONTACT

+442033132243; keenan.saleh10@imperial.ac.uk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 9, 2025
• First Posted: July 24, 2025
• Study Start: August 1, 2025
• Primary Completion (Estimated): March 1, 2027
• Study Completion (Estimated): March 1, 2027
• Last Updated: July 24, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)