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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
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | At completion of recruitment, anticipated at 18 months |
| Relative performance of the AI-ECG algorithm compared to manual localisation algorithms |
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Inclusion Criteria:
Exclusion Criteria:
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Patients referred for an electrophysiology study +/- ablation, with evidence of previous pre-excitation on their 12-lead ECG.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Keenan Saleh, MBBS | Contact | +442033132243 | keenan.saleh10@imperial.ac.uk |
| Name | Affiliation | Role |
|---|---|---|
| Ahran Arnold, PhD | Imperial College London | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Imperial College Healthcare NHS Trust | London | W12 0HS | United Kingdom |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Aug 7, 2024 | Jul 8, 2025 | Prot_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Aug 7, 2024 | Jul 18, 2025 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D011226 | Pre-Excitation Syndromes |
| ID | Term |
|---|---|
| D001145 | Arrhythmias, Cardiac |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D000075224 | Cardiac Conduction System Disease |
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Difference in performance/accuracy between the AI algorithm and pre-specified, established manual localisation algorithms (Arruda, Milstein, Pambrun, Boersma, D'Avila and Chiang) |
| 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 | At completion of recruitment, anticipated at 18 months |