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

Area of Science:

• Bioelectronic engineering
• Artificial intelligence in medicine
• Regenerative medicine

Background:

• Bioelectronic sensors enable real-time wound healing monitoring.
• Personalized treatment adjustment is hindered by complex drug interactions and lack of predictive models.

Purpose of the Study:

• To develop an adaptive closed-loop control framework for real-time wound healing treatment optimization.
• To accelerate wound closure using artificial intelligence and optimal control strategies.

Main Methods:

• Integration of deep learning, optimal control, and reinforcement learning.
• Development of a data-driven, adaptive control system for wound healing.
• In silico and in vivo experimental validation.

Main Results:

• The proposed framework accelerated wound healing by 17.71% in silico.
• Demonstrated successful convergence of the adaptive controller.
• In vivo porcine model showed accelerated inflammation and proliferation phases.

Conclusions:

• The AI-driven framework offers a model-free approach to optimize wound healing treatments.
• This technology holds significant translational potential for personalized wound care.
• The system effectively guides treatment to promote faster wound closure.