A Bioelectrically Enabled Smart Bandage for Accelerated Wound Healing and Predictive Monitoring.

Area of Science:

• Bioelectronics
• Wound Healing
• Medical Devices

Background:

• Chronic wounds present a major healthcare challenge due to slow healing and infection risk.
• Smart bandages integrating electrical stimulation and monitoring offer a novel therapeutic strategy.
• Electric fields (EF) show potential for enhancing tissue regeneration.

Purpose of the Study:

• To evaluate the efficacy of EF-enabled smart bandages in accelerating chronic wound healing.
• To assess the real-time physiological monitoring capabilities of the smart bandage.
• To develop machine learning models for predicting wound healing status.

Main Methods:

• Full-thickness excisional wounds in rats were treated with a smart bandage delivering a 200 mV/mm EF for 5 hours daily over 14 days.
• Continuous monitoring of vital signs (heart rate, SpO2, temperature) was performed.
• Machine learning models (XGBoost, Random Forest) were trained using physiological data and SHAP analysis for feature importance.

Main Results:

• EF treatment significantly accelerated wound closure by Day 7 (82.0% vs 70.75%) and Day 14 (0.01 cm² vs 0.24 cm²).
• Histological analysis confirmed enhanced neovascularization, collagen alignment, and epithelial regeneration in the EF group.
• Machine learning models achieved >98% accuracy in predicting healing status, with EF exposure and duration as key predictors.

Conclusions:

• EF-based smart bandages significantly promote chronic wound healing.
• The integrated system allows for accurate prediction of wound healing outcomes using machine learning.
• This bioelectronic approach demonstrates high potential for clinical translation in advanced wound care.