Automatic wound detection and size estimation using deep learning algorithms.

Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics -such as wound size progress- relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.

Topical Fluoxetine as a Novel Therapeutic That Improves Wound Healing in Diabetic Mice.

Diabetic foot ulcers represent a significant source of morbidity in the U.S., with rapidly escalating costs to the health care system. Multiple pathophysiological disturbances converge to result in delayed epithelialization and persistent inflammation. Serotonin (5-hydroxytryptamine [5-HT]) and the selective serotonin reuptake inhibitor fluoxetine (FLX) have both been shown to have immunomodulatory effects. Here we extend their utility as a therapeutic alternative for nonhealing diabetic wounds by demonstrating their ability to interact with multiple pathways involved in wound healing. We show that topically applied FLX improves cutaneous wound healing in vivo. Mechanistically, we demonstrate that FLX not only increases keratinocyte migration but also shifts the local immune milieu toward a less inflammatory phenotype in vivo without altering behavior. By targeting the serotonin pathway in wound healing, we demonstrate the potential of repurposing FLX as a safe topical for the challenging clinical problem of diabetic wounds.