University of Arkansas Professor Awarded $1.6M For Wound Care Project

University of Arkansas Professor Awarded $1.6M For Wound Care Project
University of Arkansas logo. A professor there has won a $1.6 grant for a chronic wound imaging project. (University of Arkansas)

A University of Arkansas professor has received a four-year, $1.6 million grant from the National Institutes of Health to develop non-invasive, real-time “optical biopsies” of chronic skin wounds.

The goal of biomedical engineering professor Kyle Quinn and his team is to provide digital histopathology images — the microscopic examination of tissue to study the manifestation and progression of disease — and other quantitative information without the need for an invasive biopsy, tissue processing and staining with histology dyes, the university said.

Chronic wounds include pressure ulcers, diabetic foot ulcers, venous stasis ulcers and arterial insufficiency ulcers.

Quinn has been working for several years on an alternative, quantitative imaging system that addresses some limitations of conventional histological analysis. His team uses multiphoton microscopy to view tissue in three dimensions at the cellular level and generate 3D maps of wound metabolism.

In addition, the researchers have partnered with Justin Zhan, a UA professor of computer science and computer engineering, to combine multiphoton microscopy and deep learning, an artificial intelligence-based approach to analysis.

“Through deep learning we can train a computer algorithm to delineate wound regions accurately and very quickly,” Quinn said in a news release. “This will greatly speed up our analysis and remove the subjectivity and bias that is inherent when you ask humans to assess images and identify features.”

He will also collaborate with Aristidis Veves, research director of the Joslin-Beth Israel Deaconess Foot Center, and Marjana Tomic Canic, professor of dermatology at the University of Miami. By combining wound image data from multiple labs, Quinn's team will have a more diverse set of data to rigorously train neural networks that can broadly work for different kinds of wounds, the university said.