Up to 1.1 million Americans per year suffer from burn injuries that require medical care. The foundation of burn wound care is determining whether a patient has a deep burn that will require excision and skin grafting or a partial thickness burn that is expected to heal without surgery and without scarring. Accurate determination of burn wound depth is critical because over estimation results in unnecessary surgery and under estimation results in prolonged treatment, increased cost, increased scarring, disfigurement and loss of function. Clinical assessment is most commonly used to determine burn wound depth, but is not very reliable, and even with the most experienced surgeons is only accurate 76% of the time. A variety of imaging modalities have been used to try and estimate burn wound depth. The current gold standard is laser Doppler imaging (LDI), that correlates blood flow with burn wound depth. It has not been widely adopted in the US. Barriers include difficulty interpreting the images, and multiple variables that interfere with light based detection of blood flow including residual pigments from silver dressings or tattoos, and patient conditions that affect blood flow such as infection, edema and use of vasoactive medicines. We propose to detect burn-induced changes in the mechanical properties of the subcutaneous tissue using FDA approved tissue Doppler elastography imaging (TDI). The premise is that changes caused by burn injury that have propagated beyond the skin will change the stiffness of the subcutaneous tissue. Those changes can be detected by changes in color (red= soft, blue = hard) in the image. Using sound waves to detect tissue changes avoids the limitations of light based imaging technologies such as LDI. To promote adoption of this technology, Med Compliance IQ will develop a software platform called Burn IQ that uses neural networks to analyze the TDI images. It will provide clinical decision support by providing a probability that a burn injury is deep, i.e. requires excision and grafting, vs. partial thickness. Med Compliance IQ has partnered with a team at Ohio State University that established model of pig burn injury that has been characterized with TDI. For phase 1 of the SBIR proposal the pig model will be used for software planning and development by obtaining TDI images from known partial thickness burns, known full thickness burns and burns of indeterminate thickness. Initial testing and refinement of the software technology will also be done with human subject that also have known partial thickness, known full thickness and indeterminate depth burns based on healing outcomes. This information will be used to yield a software product ready for testing on a large scale in human subjects during Phase 2 of the SBIR. Development of this product will improve diagnostic accuracy to achieve better outcomes for burn patients.

Public Health Relevance

Optimal outcomes for people with burn wound injuries depends on accurate determination of burn wound depth. Tissue Doppler elastography imaging can detect burn-induced changes in tissue stiffness to aid in assessing burn wound depth. Burn IQ software technology will use neural networks to interpret tissue Doppler elastography images to improve diagnostic accuracy and achieve better clinical outcomes.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1)
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Cole, Alison E
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Med-Compliance Iq, Inc.
United States
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