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Related Experiment Video

Updated: Sep 13, 2025

Chessboard-like Burn Wound Healing Model of Mice Based on Digital Heating Device
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Accelerating Wound Healing Through Deep Reinforcement Learning: A Data-Driven Approach to Optimal Treatment.

Fan Lu1, Ksenia Zlobina1, Prabhat Baniya2

  • 1Applied Mathematics, Baskin Engineering, University of California Santa Cruz, Santa Cruz, CA 95060, USA.

Bioengineering (Basel, Switzerland)
|July 29, 2025
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Summary

This study introduces an adaptive AI framework for real-time wound healing treatment. The novel approach accelerates wound closure by 17.71% using deep reinforcement learning without complex biological models.

Keywords:
biomedical engineeringdeep reinforcement learningwound healing

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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.