AI and single-cell tests to predict Crohn's disease course in children
Predicting Clinical Phenotypes in Crohn's Disease Using Machine Learning and Single-Cell 'omics
This project uses artificial intelligence plus single-cell and other molecular data to predict which children and teens with Crohn's disease will have mild versus severe or treatment‑resistant disease.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Duke University NIH-funded |
| Lab location | 1 site (Durham, United States) |
| Project ID | NIH-11404078 on NIH RePORTER |
What this research studies
You would be part of efforts that combine clinical records, tissue biopsy images, and detailed single-cell molecular measurements to teach computer models how Crohn's disease progresses. Researchers will use archived and new patient samples and data, apply automated image analysis and machine learning to extract tissue features, and build predictive algorithms. Models will be tested and validated to see how well they identify kids likely to respond to standard anti‑TNF therapy or to develop strictures or penetrating complications. The goal is a tool that could help clinicians choose the right treatment earlier for each child.
Who could benefit from this research
Good fit: Ideal candidates are children and adolescents with a diagnosis of Crohn's disease—especially those recently diagnosed or who have available biopsy tissue and clinical records for analysis.
Not a fit: Adults without pediatric-onset Crohn's, people without available biopsy samples or clinical data, and those with other digestive diseases like ulcerative colitis are unlikely to be included or directly benefit.
Why it matters
Potential benefit: If successful, this work could help doctors personalize treatment earlier, reducing unnecessary delays to stronger therapy or surgery for children likely to develop severe disease.
How similar studies have performed: Previous prediction models using only clinical data had limited accuracy, and combining single-cell 'omics with machine learning is a promising but still relatively new approach.
Where this research is happening
Durham, United States
- Duke University — Durham, United States (Active)
Researchers
- Principal investigator: Syed, Sana — Duke University
- Study coordinator: Syed, Sana
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.