Automated Quantitative Analysis of Wound Histology Using Deep-Learning Neural Networks

Abstract

Every year, ∼8 million Americans require advanced care for nonhealing wounds that collectively are estimated to cost between $28–96 billion (Sen, 2019). Complications in healing disproportionally afflict the elderly who commonly suffer from comorbidities, such as vascular insufficiency and diabetes mellitus (Gosain and DiPietro, 2004; Gould et al., 2015), that disrupt wound closure. Histological staining of skin tissue sections with H&E can provide insight into cellular infiltration into the wound, infection, hyperproliferation at the edge of the wound, or fibrosis and serves as a critical technique in the research laboratory to understand wound pathophysiology and evaluate new wound care products (Eming et al., 2014; Gantwerker and Hom, 2012).