Accelerating Wound Healing Through Deep Reinforcement Learning: A Data-Driven Approach to Optimal Treatment

Abstract

Advancements in bioelectronic sensors and actuators have paved the way for real-time monitoring and control of wound healing progression. Real-time monitoring allows for precise adjustment in treatment strategies that align with an individuals unique biological response. However, due to the complexities of human-drug interactions and a lack of predictive models it is challenging to determine just how one should adjust drug dosage to achieve the desired biological response. This work proposes an adaptive closed-loop control framework that integrates deep learning, optimal control, and reinforcement learning to update treatment strategies in real-time with the goal of accelerating wound closure. The proposed approach eliminates the need for mathematical modeling of complex nonlinear wound healing dynamics. We demonstrate the convergence of the controller via an in silico experimental setup, where the proposed approach successfully accelerates the wound healing process by 17.71%. Finally, we share the experimental setup and results of an in vivo implementation to highlight the translational potential of our work. Our data-driven model estimates a 40% acceleration in wound closure.