# Clinical Evaluation of Burns using Spatial Frequency Domain Imaging

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2020 · $430,325

## Abstract

Program Director/Principal Investigator (Last, First, Middle): Durkin, Anthony J.
Abstract
The central aim of this 3 year competing R01 renewal is to characterize and apply a new, compact, clinic-
friendly Spatial Frequency Domain Imaging (SFDI) device to objectively and non-invasively classify burn
severity (burn grade) over a large areas of skin. Delays in determining burn severity directly impacts patient
treatment plans (including decisions whether to graft), rates of infection and scarring, duration of hospitalization
and ultimately cost of care. Currently, the primary method of determining burn severity continues to be clinical
assessment, which is highly subjective. While both superficial thickness and full-thickness burns are typically
readily diagnosed based on visual clinical impression, partial thickness burns are difficult to classify and carry
with them considerable potential for complications. Burn severity classification accuracy, even by experts, is
only 60–80%. Our research in animal models demonstrates that SFDI data can successfully be used to classify
different regions of burn severities. Typically, these differences are not apparent to the unaided eye and a
great deal of training and experience is required in order for clinicians to accurately differentiate them Our work
using a research grade, hybrid-SFDI device suggests that objective parameters provided by SFDI can be used
within 24 hours after injury, to accurately classify burn severity. Specifically, we have demonstrated in a
porcine burn model that the research grade SFDI outperforms laser speckle imaging and thermal imaging at 24
hours post-burn, in terms of predicting whether a burn will require a graft or not. However, translating these
results to the clinic has been difficult due to several device limitations. The research grade SFDI device has
slow acquisition times that can result in motion artifacts. It is also sensitive to ambient light which is often an
issue in a clinical setting. Additionally, the SFDI device generates so much diverse data (oxygenated and
deoxygenated hemoglobin, water fraction, reduced scattering coefficients at multiple wavelengths), there is no
obvious way to present it to a clinical user to make a quick decision. To this end, we propose to methodically
investigate an improved next generation SFDI device that addresses these issues by using brighter LEDs and
fewer wavelengths to rapidly collect data in a way that reduces motion artifacts and is independent of clinical
lighting conditions. In addition, we will develop a machine learning based classification framework that will
provide the clinical with actionalble diagnostic information. The central aim of this 3 year competing R01
renewal is to characterize and then modify a new clinic-friendly SFDI device (Clarifi) to objectively classify in-
vivo regions of different burn severity over large areas. The proposed research seeks to investigate this via the
following Specific Aims: 1) Test ...

## Key facts

- **NIH application ID:** 10052657
- **Project number:** 2R01GM108634-05A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** ANTHONY J DURKIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $430,325
- **Award type:** 2
- **Project period:** 2014-09-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10052657

## Citation

> US National Institutes of Health, RePORTER application 10052657, Clinical Evaluation of Burns using Spatial Frequency Domain Imaging (2R01GM108634-05A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10052657. Licensed CC0.

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