AI to personalize treatment for ventricular arrhythmias
Machine Learning for Ventricular Arrhythmias
This project uses artificial intelligence and patient-specific heart models to predict which people with ventricular tachycardia or fibrillation will benefit from anti-arrhythmic drugs or ablation.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11401678 on NIH RePORTER |
What this research studies
This work combines machine learning on large patient registries with computer simulations of individual hearts to tailor care for people with ventricular tachycardia (VT) or fibrillation. Researchers will use bedside data, lab results, and non-invasive imaging to train models and run simulations that estimate whether a patient's heart can sustain VT before and after treatments. Predictions will be validated using large external registries from multiple institutions to test generalizability. The approach aims to move treatment decisions away from trial-and-error toward data-driven choices about drugs and ablation.
Who could benefit from this research
Good fit: People with ventricular tachycardia or ventricular fibrillation who are being considered for anti-arrhythmic medications or catheter ablation, or who are enrolled in participating registries, are the most likely candidates.
Not a fit: People without VT/VF or whose clinical data are not captured by the contributing registries are unlikely to receive direct benefit from this project.
Why it matters
Potential benefit: If successful, this could help doctors choose the right medicines or decide who should get ablation, reducing unnecessary procedures and preventing cardiac arrest.
How similar studies have performed: Related machine-learning efforts in cardiology have shown promise at predicting outcomes, but combining large-registry AI with patient-specific heart simulations for VT treatment selection is relatively novel and largely untested clinically.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Narayan, Sanjiv M — Stanford University
- Study coordinator: Narayan, Sanjiv M
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.