# Automatic analysis of 3D skin images for chronic graft-versus-host disease (cGVHD) severity assessment

> **NIH NIH R21** · VANDERBILT UNIVERSITY · 2020 · $201,692

## Abstract

Project Summary
More than 20,000 hematopoietic stem cell transplants (including bone marrow transplants) are performed in the
U.S. each year to cure a range of diseases ranging from leukemias to sickle cell anemia to autoimmune
deficiencies in children. Unfortunately, most long-term non-relapse survivors will die of chronic graft-versus-host
disease (cGVHD), which remains a disease of steadily increasing incidence and profound unmet need. A
fundamental barrier in cGVHD management and research is a lack of sensitive and objective assessment tools
that permit objective and reproducible measures of disease severity and progression. Skin is the most commonly
affected organ in cGVHD and automated techniques capable of measuring precisely the surface area of involved
skin in photographs may provide the tools necessary for effectively evaluating patient progress. We propose to
(1) create the data set necessary to develop machine learning-based methods for the automatic analysis of
cGVHD images, and (2) implement and evaluate these methods.

## Key facts

- **NIH application ID:** 9864040
- **Project number:** 5R21AR074589-02
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** BENOIT M. DAWANT
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $201,692
- **Award type:** 5
- **Project period:** 2019-02-07 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9864040

## Citation

> US National Institutes of Health, RePORTER application 9864040, Automatic analysis of 3D skin images for chronic graft-versus-host disease (cGVHD) severity assessment (5R21AR074589-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9864040. Licensed CC0.

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