Research promises new information on skin lesions | Colleges and Universities
FAYETTEVILLE – Biomedical engineering professor Kyle Quinn 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 Quinn and researchers in her lab is to provide digital histopathologic images – the microscopic examination of tissue to study disease manifestation and progression – and other quantitative information without the need for an invasive biopsy. , tissue treatment and staining with histological stains.
CHRONIC SKIN WOUNDS
Chronic wounds are skin lesions that do not progress in the normal healing process. There are many types of chronic wounds, including pressure sores, diabetic foot ulcers, venous stasis ulcers, and arterial insufficiency ulcers. These non-healing wounds can have different underlying causes, but are often characterized by tissue inflammation, poor blood circulation, callus formation, or infection. They affect more than 150 million people worldwide and cost an estimated $ 50 billion in healthcare per year in the United States alone.
Initial clinical evaluation of a chronic skin wound involves visual inspection, but more detailed characterization relies on histological analysis of tissue biopsies from the wound. Although this approach is useful in clinics and research laboratories to understand the pathophysiology of wounds and to develop new products to treat chronic skin wounds, it is inherently invasive, time consuming and qualitative.
For several years, Quinn has been working on an alternative quantitative imaging system that meets certain limitations of conventional histological analysis. Researchers in his lab use multiphoton microscopy to visualize three-dimensional tissue at the cellular level and generate 3D maps of wound metabolism. This imaging technique is non-invasive, allowing them to measure changes in cell metabolism and skin organization over time within the same wounds.
DEEP LEARNING AND AI
Although their metabolic imaging technique can provide very detailed assessments of cell function, analyzing their image data takes time. Researchers have to manually map relevant image regions to specific skin layers or wound regions, which is a slow and tedious process. To speed it up, Quinn teamed up with Justin Zhan, a professor of computer science and computer engineering. Zhan, an expert in data science, helps Quinn combine multiphoton microscopy and deep learning, an analysis approach based on artificial intelligence.
Deep learning is a subset of machine learning, which uses computer algorithms to extract meaningful information from data, using neural networks inspired by the organization of the neurons that make up the human brain. Deep learning uses computer algorithms to train neural networks with multiple layers, which allows the algorithm to learn more complex tasks. The deep learning approach will enable Quinn to provide rapid quantitative analysis of chronic skin wounds.
âThrough deep learning, we can train a computer algorithm to delineate wound regions precisely and very quickly,â Quinn said. “This will significantly speed up our analysis and remove the subjectivity and inherent bias when asking humans to rate images and identify features.”
Quinn and Jake Jones, a former PhD student at Quinn, published preliminary results on the use of deep learning to identify wound characteristics in the Journal of Investigative Dermatology and Lasers in Surgery and Medicine. Jones, who received his doctorate. and now working as a product manager for an optical manufacturing company, did most of the preliminary work.
In addition to Zhan, Quinn will collaborate with leaders in the wound healing field, including Aristidis Veves, research director at 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, the team will have a more diverse dataset to rigorously train neural networks that can work broadly for different types of wounds.