Lu Fan, Zlobina Ksenia, Rondoni Nicholas A, Teymoori Sam, Gomez Marcella
Applied Mathematics, Baskin School of Engineering, University of California, Santa Cruz, CA, USA.
R Soc Open Sci. 2024 Jul 31;11(7):240228. doi: 10.1098/rsos.240228. eCollection 2024 Jul.
Finding the optimal treatment strategy to accelerate wound healing is of utmost importance, but it presents a formidable challenge owing 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 agent for tracking the optimal signal derived from this representation without the need for intricate mathematical modelling, 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 modelling and optimal adaptive control, eliminating the need for intricate mathematical models.
找到加速伤口愈合的最佳治疗策略至关重要，但由于该过程固有的非线性性质，这带来了巨大挑战。我们提出了一种自适应闭环控制框架，该框架结合了深度学习、最优控制和强化学习来加速伤口愈合。通过使用深度学习自适应地学习非线性伤口愈合动力学的线性表示，并交互式训练深度强化学习智能体以跟踪从该表示中得出的最优信号，而无需复杂的数学建模，我们的方法不仅与未进行任何治疗的情况相比成功地将伤口愈合时间缩短了45.56%，而且还展示了提供更安全、更经济的治疗策略的优势。所提出的方法通过有效整合感知、预测建模和最优自适应控制，消除了对复杂数学模型的需求，展现出加速伤口愈合的巨大潜力。
