# Artificial intelligence to estimate extent of cGVHD from patient photos

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $834,692

## Abstract

PROJECT SUMMARY / ABSTRACT
Lack of reliable assessment tools is a major obstacle to validating novel treatments for chronic graft-versus-
host disease (cGVHD), the leading cause of long-term morbidity and mortality after allogeneic hematopoietic
stem cell transplantation (HCT) to cure life-threatening blood diseases. Skin erythema is a key cGVHD
biomarker, but standard of care in-person evaluations are subjective, prone to error, and costly. We will
validate an artificial intelligence (AI) technology for accurate, efficient, and easily deployable measurement of
cGVHD erythema from photographs in diverse patient populations.
In our preparatory study, AI technology achieved human-level performance under controlled photography
conditions. We propose to refine and validate this AI technology in a multicenter cGVHD patient cohort of
unprecedented size, leveraging experts in dermatology, transplant medicine, medical imaging, artificial
intelligence, biomedical informatics, and data science. A unique database of over 11,000 photographs and
clinical information from ethnically and phenotypically diverse patients will be assembled from five major
cancer centers: Fred Hutchinson Cancer Center, Mayo Clinic, the National Institutes of Health, University of
Pennsylvania, and Vanderbilt University Medical Center. Accuracy to measure cGVHD erythema will be
determined relative to expert dermatologist-level assessments. We will quantify and overcome potential biases
for the AI including skin tone, gender, photography conditions, and disease severity. We will compare the
prognostic value of AI and human assessments as biomarkers of mortality. Finally, we will prospectively
benchmark the accuracy of AI measurements against standard in-person clinical trial assessments (NIH Skin
Scoring) for patients recruited at all five cancer centers.
The proposal could improve patient care and telemedicine through: consistent cGVHD scoring equivalent to
specialist examination; visualizing cutaneous changes for quality assurance in observational and therapeutic
studies; enabling frequent longitudinal monitoring at home or in clinic; and relieving the burden of time-
consuming skin area assessment on patient care providers.
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## Key facts

- **NIH application ID:** 10879661
- **Project number:** 1R01HL169944-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Eric R Tkaczyk
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $834,692
- **Award type:** 1
- **Project period:** 2024-05-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10879661

## Citation

> US National Institutes of Health, RePORTER application 10879661, Artificial intelligence to estimate extent of cGVHD from patient photos (1R01HL169944-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10879661. Licensed CC0.

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