Central Sensitization in Urologic Pain: A Machine-Learning Approach
Official title: Phenotyping and Validating Central Sensitization in Painful and Non-Painful Benign Urologic Disease Using Machine Learning
Funded at Fairleigh Dickinson University · NIH-11445509 · Data updated 2026-11-12
Scientists will use machine learning on existing patient data to find a central nervous system-based pattern linked to bladder pain conditions, with the goal of guiding personalized treatments for people with IC/BPS and OAB.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Fairleigh Dickinson University NIH-funded |
| Lab location | 1 site (Teaneck, United States) |
| Project start | August 17, 2026 |
| Project end | July 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $560,491 |
| NIH project number | 1R15AT014052-01 |
| Project ID | NIH-11445509 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with interstitial cystitis/bladder pain syndrome or overactive bladder who have undergone prior assessments and are represented in the existing datasets.
✕Less likely to help
Adults without CS-related patterns or those whose data do not reflect central sensitization may not experience a direct benefit.
What this research studies
This project analyzes already collected data from people with interstitial cystitis/bladder pain syndrome and overactive bladder to uncover a central sensitization pattern. By applying advanced modeling to quantitative sensory tests and pain ratings, researchers aim to define a CS phenotype that could predict who may respond to specific therapies. The work also builds a student-friendly research infrastructure across two universities to sustain future collaboration. The outcome could inform customized interventions that target the brain–bladder connection. The study emphasizes data-driven, mechanism-informed approaches rather than new medications alone.
Why it matters
Potential benefit: If successful, patients could receive more precise, personalized treatments based on their CS profile, potentially reducing pain and urinary symptoms.
How similar studies have gone: Prior research using ML on pain data has shown promise in identifying CS-related patterns, but applying this to urologic conditions is an emerging area.
Where this research is happening
-
Fairleigh Dickinson University
Teaneck, United States Active
Researchers
- Principal investigator: Johnson, Benjamin Norman — Fairleigh Dickinson University
- Study coordinator: Johnson, Benjamin Norman
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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.