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NCT05967546: ECG-LEARNING
Deep Learning for Intelligent Identification of Arrhythmias
trial testing Observational in Arrhythmia in 4,000 participants. Not yet recruiting.
31 August 2028
Quick facts
| Lead sponsor | First Affiliated Hospital Xi'an Jiaotong University |
|---|---|
| Status | Not yet recruiting |
| Study type | OBSERVATIONAL |
| Enrollment | 4,000 |
| Start date | 30 December 2024 |
| Primary completion | 31 August 2028 |
| Estimated completion | 31 December 2028 |
| Sites | 1 location across China |
Drugs / interventions tested
- Observational — full drug profile →
Conditions studied
- Arrhythmia — all drugs for Arrhythmia →
Sponsor
First Affiliated Hospital Xi'an Jiaotong University
Who can join
3 and older, any sex, with Arrhythmia. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT05967546
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT05967546 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by First Affiliated Hospital Xi'an Jiaotong University
- Last refreshed: 4 April 2024
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT05967546.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing