Image-based tools to spot post-cesarean wound infections across different populations
Official title: Image-based algorithms for remote cesarean surgical site infection diagnoses in diverse populations
Funded at Univ of North Carolina Chapel Hill · NIH-11478410 · Data updated 2026-11-12
Aims to test two image-based computer algorithms to detect infections after cesarean birth, using real patient images from diverse sites to help earlier care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | August 15, 2024 |
| Project end | May 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $116,368 |
| NIH project number | 3R01HD112403-03S2 |
| Project ID | NIH-11478410 on NIH RePORTER |
Who could benefit from this research
✓Could help
Women who have recently had a cesarean delivery in participating sites (Rwanda, Ghana, Mexico) and who can provide wound images and follow-up data.
✕Less likely to help
Patients whose wounds do not show signs of infection or whose data cannot be appropriately captured may not benefit.
What this research studies
This project will validate and update two existing algorithms that analyze wound photos and thermal images to detect surgical site infections after cesarean deliveries. It plans to enroll 6,000 women across Rwanda, Ghana, and Mexico, collecting wound images and infection diagnoses around postoperative day 10. By examining how well the visible and thermal image tools work in diverse skin tones and real-world settings, the study seeks to improve remote monitoring through community health programs. The goal is to determine if these AI tools can generalize beyond their original rural Rwanda data to help clinicians and caregivers identify infections sooner.
Why it matters
Potential benefit: If successful, the tools could enable timely, remote detection of cesarean wound infections, potentially reducing complications and improving recovery.
How similar studies have gone: Previous algorithms showed strong performance in initial sites, with varying sensitivity/specificity, and this study tests broader generalizability.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Hedt-Gauthier, Bethany — Univ of North Carolina Chapel Hill
- Study coordinator: Hedt-Gauthier, Bethany
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This project uses photos and thermal images to help healthcare workers detect wound infections after cesarean delivery in women from Rwanda, Ghana, and Mexico.
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.