# Image-based algorithms for remote cesarean surgical site infection diagnoses in diverse populations

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2024 · $741,747

## Abstract

Project Summary/Abstract
Post-cesarean surgical site infections (SSIs) contribute to maternal morbidity and mortality globally; as rates of
cesarean delivery increase, so will the number of SSIs. Timely SSI diagnosis and treatment can improve
maternal outcomes. However, in many settings, particularly in rural areas, postoperative wound monitoring is
challenging due to physical and financial barriers.
The overall goal of this proposal is to improve strategies for post-cesarean SSI monitoring by validating and
updating two existing image-based diagnostic algorithms. The original algorithms were trained on image-SSI
diagnosis dyads collected on women delivering via cesarean in rural Rwanda. One algorithm, using visible
images (photographs), had a 83% sensitivity and 75% specificity. The second algorithm, using thermal images,
had 95% sensitivity and 84% specificity.
In this proposed research, we will prospectively follow 6,000 women in Rwanda, Ghana and Mexico (2,000 per
site) and collect wound images and SSI diagnoses at postoperative day (POD) 10. These sites were chosen
because of: a) high SSI rates; b) the potential to integrate an accurate SSI diagnostic algorithm into existing
community health worker follow-up programs; and c) the diversity in skin tones across the study sites. Using this
data, we will assess the generalizability of the existing visible image and thermal image algorithms, evaluating
the sensitivity and specificity overall and by country (Aim 1). We will then retrain the algorithm to improve
predictive properties across diverse populations, and we will explore adding clinical data to the algorithms to
improve accuracy (Aim 2). Finally, for a subset of 1,200 women who are SSI negative at POD10, we will
reevaluate at POD20 and POD30, and use these image-SSI diagnosis dyads to explore the need for later SSI
monitoring and the ability to predict delayed SSIs with images captured at POD10 (Aim 3).
The culmination of this research will provide strategies for home-based monitoring of cesarean-related SSIs that
can accommodate a range of skin tones.

## Key facts

- **NIH application ID:** 10979935
- **Project number:** 1R01HD112403-01A1
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Bethany Hedt-Gauthier
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $741,747
- **Award type:** 1
- **Project period:** 2024-08-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979935

## Citation

> US National Institutes of Health, RePORTER application 10979935, Image-based algorithms for remote cesarean surgical site infection diagnoses in diverse populations (1R01HD112403-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10979935. Licensed CC0.

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