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NCT Number: NCT07083791

AI-ECG Accessory Pathway Localisation Study

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

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Key information

Conditions

Accessory Pathway Arrhythmias, Cardiac Artifical Intelligence Cardiac Conduction System Disease Cardiovascular Diseases ECG Heart Diseases Pre-Excitation Syndromes

Age range

13 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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

    Time frame: 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 outcomes

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

    Time frame: 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

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

    Time frame: 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)

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

    Time frame: 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

Study contacts

Contact information is provided by the study sponsor or research team.

Keenan Saleh, MBBS

CONTACT

[email protected]

+442033132243

Sponsors and collaborators

Lead sponsor

Imperial College London

Other

Registry information

Acronym: AAPLS

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Jul 24, 2025
Registry last updated
Jul 24, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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