AI ECG Analysis to Predict Deadly Arrhythmias and ICI Myocarditis (ELDORA) — a clinical trial (ClinicalTrials.gov)

An electrocardiogram (ECG) records the heart's electrical activity as a waveform. It is inexpensive, non-invasive, and generated in enormous volumes. Yet early warning signs of life-threatening arrhythmias — such as long QT syndrome and Torsades-de-Pointes — or signs of myocarditis triggered by the immune checkpoint inhibitors used in cancer immunotherapy can appear as subtle shifts in the trace that are easy for the human eye to miss. Artificial intelligence (AI), which can learn fine-grained patterns from large numbers of ECGs, is hoped to help surface such hard-to-see risks earlier, and this study aims to develop and validate predictive models toward that goal.

A defining feature of this work is that it is observational rather than interventional: it analyzes already-collected ECGs and de-identified clinical information instead of testing a new treatment. ECGs that arrive in different formats from different sites are standardized into a common form and assembled into a harmonized database named ECGInsight, after which the project aims for interpretable models whose reasoning a human can follow. Because clinical adoption of medical AI depends on transparency and verifiability as much as on accuracy, the emphasis on data harmonization and explainability addresses the practical hurdles this field actually faces.

Seen more broadly, the study is also an example of unlocking medical data that often sits idle, pooling and reusing multiple cohorts across countries rather than relying on a single site. As cancer immunotherapy becomes more widespread and monitoring for cardiac side effects grows more important, using AI to extend the reach of a cheap, ubiquitous test could help with the public-health challenge of detecting rare but fatal events early. Even so, what is described here is the study's aim and hypothesis; it does not mean the effectiveness or safety of the AI has been established.