Accessory PathwayAAPLSNCT07083791
AI-ECG Accessory Pathway Localisation Study
An observational study of Accessory Pathway and Artifical Intelligence, run by Imperial College London at 1 site in United Kingdom.
Trial 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 Read more about this study
- Who is it for?
- Ages 13 to 100 years
- Conditions the study is for
- Accessory Pathway, Artifical Intelligence, ECG
- How many people
- 100 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Aug 2025to 1 Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 13 to 100 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients referred for an electrophysiology study +/- ablation, with evidence of previous pre-excitation on their 12-lead ECG.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site
Imperial College Healthcare NHS Trust
London, W12 0HS
Keenan Saleh, MBBS, 02033132243, k.saleh@nhs.net
Ahran Arnold, PhD, 02033132243, ahran.arnold@imperial.ac.uk
About this 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
Read the 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.
- Conditions
- Accessory Pathway, Artifical Intelligence, ECG
- Keywords
- AI-ECG, AI, ECG, Accessory pathway, Accessory pathway localisation
- Medical subject headings
- Pre-Excitation Syndromes
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At completion of recruitment, anticipated at 18 months
Secondary outcomes (3)
Relative performance of the AI-ECG algorithm compared to human estimation
Difference in performance/accuracy between the AI-ECG accessory pathway localisation algorithm and human estimation from the 12-lead ECG
Measured: At completion of recruitment, anticipated at 18 months
Relative performance of the AI-ECG algorithm compared to manual localisation algorithms
Difference in performance/accuracy between the AI algorithm and pre-specified, established manual localisation algorithms (Arruda, Milstein, Pambrun, Boersma, D'Avila and Chiang)
Measured: At completion of recruitment, anticipated at 18 months
Accuracy of the ground truth locations from the human operator compared to the successful ablation location
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
Measured: At completion of recruitment, anticipated at 18 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jul 2025
- Study start1 Aug 2025 (estimated)
- Primary completion1 Mar 2027 (estimated)
- Study completion1 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Accessory PathwayStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ahran Arnold, PhD, Principal investigator, Imperial College London
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07083791
- Sponsor's protocol ID
- IRAS 347921
- Acronym
- AAPLS
- Status last verified
- Jul 2025
- First submitted
- 9 Jul 2025
- Last updated
- 24 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-ECG Accessory Pathway Localisation Study
- ClinicalTrials.gov ID
- NCT07083791
- Acronym / sponsor ID
- AAPLS / IRAS 347921
- Conditions
- Accessory Pathway, Artifical Intelligence, ECG
- Keywords
- AI-ECG, AI, ECG, Accessory pathway, Accessory pathway localisation
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Study start
- 1 Aug 2025 (estimated)
- Primary completion
- 1 Mar 2027 (estimated)
- Study completion
- 1 Mar 2027 (estimated)
- First posted
- 24 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 13 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients referred for an electrophysiology study +/- ablation, with evidence of previous pre-excitation on their 12-lead ECG.
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
3 secondary outcomes
Relative performance of the AI-ECG algorithm compared to human estimation
Time frame: At completion of recruitment, anticipated at 18 months
Relative performance of the AI-ECG algorithm compared to manual localisation algorithms
Time frame: At completion of recruitment, anticipated at 18 months
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Jul 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ahran Arnold, PhD principal investigator
Imperial College London
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Accessory Pathway and Artifical Intelligence, run by Imperial College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 24 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Keenan Saleh, MBBS (+442033132243, keenan.saleh10@imperial.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance and accuracy of the AI-ECG accessory pathway localisation algorithm (time frame: At completion of recruitment, anticipated at 18 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Jul 2025.