AI-powered ECG tools to detect deadly heart rhythms and ICI-related myocarditis
Official title: Efficient Deep Learning Approaches for the Rapid and Interpretable Detection of Deadly Arrhythmias in ECG Data
Run by Groupe Hospitalier Pitie-Salpetriere · NCT07644715 · Data updated 2026-11-12
Researchers are testing AI-powered analysis of ECG data to see if it can help flag dangerous arrhythmias and myocarditis in people who may be affected by immune checkpoint inhibitors.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 127000 (estimated) |
| Sponsor | Groupe Hospitalier Pitie-Salpetriere Academic / other |
| Locations | 1 site (Paris) |
| Status | Recruiting |
| Started | January 1, 2026 |
| Main results expected | December 31, 2029 |
| Study ends | December 31, 2029 |
| Registry record updated | June 12, 2026 |
| Trial ID | NCT07644715 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are individuals whose ECG data (digital or digitizable from scanned/paper records) are available within participating cohorts and accompanied by appropriate clinical metadata.
✕Probably not a fit
Patients without usable ECG data or missing requisite metadata may not benefit from AI model development.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
ELDORA is an observational, non-interventional project that collects and harmonizes ECG data from many cohorts to develop interpretable AI models. The study focuses on predicting risk for torsades de pointes and long QT syndrome, as well as diagnosing and forecasting ICI-related myocarditis, using a standardized ECGInsight database with de-identified metadata. It aims to digitize diverse ECGs, train models on large-scale data, and keep AI outputs for research/model development rather than guiding patient care during the study. The work involves collaboration across multiple international groups and includes data from healthy individuals, long QT populations, cancer patients treated with ICIs, and other cardiovascular cohorts.
Why it matters
Potential benefit: If successful, the AI tools could improve research and future decision-making around dangerous arrhythmias and ICI-related myocarditis.
How similar studies have gone: Previous efforts in ECG-based AI have shown promise in signal interpretation, and this project aims to scale and harmonize data across many cohorts for broader applicability.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * subjects included in participating existing or ongoing ECG cohorts made available to ECGInsight * availability of ECG data (digital waveform or scanned/paper ECG suitable for digitization) and relevant clinical/demographic metadata * data use permitted by applicable ethical, regulatory, contractual and GDPR requirements. Exclusion Criteria: * datasets or individual records for which required approvals, data-sharing agreements, de-identification/anonymization, or minimum ECG/metadata quality requirements are not met. No interventional study treatment is assigned.
Where it's running
-
Cic-2503
Paris, France Recruiting
Study contacts
- Study coordinator: Joe-Elie Salem, MD-PhD
- Email: joe-elie.salem@aphp.fr
- Phone: 0033142178535
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.