Finding the optimal treatment strategy to accelerate wound healing is of utmost importance, but it presents a formidable challenge due to the intrinsic nonlinear nature of the process. We propose an adaptive closed-loop control framework that incorporates deep learning, optimal control, and reinforcement learning to accelerate wound healing. By adaptively learning a linear representation of nonlinear wound healing dynamics using deep learning and interactively training a deep reinforcement learning (DRL) agent for tracking the optimal signal derived from this representation without the need for intricate mathematical modeling, our approach has not only successfully reduced the wound healing time by 45.56% compared to the one without any treatment, but also demonstrates the advantages of offering a safer and more economical treatment strategy. The proposed methodology showcases a significant potential for expediting wound healing by effectively integrating perception, predictive modeling, and optimal adaptive control, eliminating the need for intricate mathematical models.

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Recommended citation: Lu Fan, Zlobina Ksenia, Rondoni Nicholas, Teymoori Sam and Gomez, Marcella. “Enhancing Wound Healing via Deep Reinforcement Learning for Optimal Therapeutics.” In Royal Society Open Science (To Appear)