Deep learning approach to diagnosing chronic headaches and facial pain
Official title: SCH: INT: Deep Causal Learning for Diagnosis of Chronic Headache and Facial Pain
Funded at Rochester Institute of Technology · NIH-11579147 · Data updated 2026-11-12
A new AI-based method aims to improve how doctors diagnose chronic headache and facial pain by using a large, long-term set of patient data.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rochester Institute of Technology NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | September 18, 2026 |
| Project end | July 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $259,551 |
| NIH project number | 1R01DE036983-01 |
| Project ID | NIH-11579147 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with chronic headaches or facial pain whose diagnosis is uncertain and who have complex medical histories.
✕Less likely to help
Patients without chronic headache or facial pain or those not experiencing diagnostic uncertainty are less likely to benefit.
What this research studies
Researchers will build a deep, dynamic causal learning framework that analyzes thousands of patient records over time to identify how different factors contribute to chronic headache and facial pain. The project uses advanced neural models and probabilistic techniques to map how symptoms, treatments, and patient characteristics influence diagnosis in a continuous timeline. The goal is to reduce diagnostic delays and uncertainty for adults with complex headache-related conditions. The work involves collaboration with a large retrospective cohort to learn patterns that could guide future clinical decision-making.
Why it matters
Potential benefit: If successful, this approach could help doctors reach more accurate diagnoses faster and tailor treatments for people with chronic headaches and facial pain.
How similar studies have gone: Previous AI-driven diagnostic models have shown promise in structured data but this project emphasizes continuous-time causal modeling in a large real-world cohort, which is a relatively new approach.
Where this research is happening
-
Rochester Institute of Technology
Rochester, United States Active
Researchers
- Principal investigator: Lee, Yangming — Rochester Institute of Technology
- Study coordinator: Lee, Yangming
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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.