Adaptive Electroceutical Wound Dressing with AI-Driven Therapy

The Need

Chronic and complex wounds remain a major clinical and economic burden, with high infection rates, slow healing trajectories, and limited real-time monitoring capabilities. Conventional dressings are largely passive and do not adapt to dynamic wound environments, while existing advanced therapies often lack continuous feedback or personalization. There is a clear need for integrated solutions that can both treat and monitor wounds in real time, enabling early intervention, improved outcomes, and reduced healthcare utilization across hospital, home, and field settings.

The Technology

An OSU engineer has developed an intelligent wound dressing platform that combines therapeutic electrical stimulation with integrated sensing and data-driven decision-making. The system continuously monitors wound conditions and applies tailored electrical signals to support infection control and tissue repair. Embedded analytics and machine learning enable real-time interpretation of wound status and adaptive therapy adjustments. The platform can operate locally or connect to external systems, enabling scalable deployment from bedside care to remote monitoring environments.

Commercial Applications

  • Connected dressings for remote patient monitoring and telehealth platforms
  • Battlefield and emergency medicine for rapid, autonomous wound stabilization
  • Advanced wound care products for chronic wounds (e.g., diabetic ulcers, pressure sores)
  • Post-surgical and burn care solutions for accelerated healing and infection management

Benefits/Advantages

  • Closed-loop therapy: Integrates sensing and treatment for real-time, adaptive care
  • Dual-function capability: Addresses both infection control and tissue regeneration within a single platform
  • Data-driven personalization: Continuously refines treatment based on patient-specific wound conditions
  • Scalable and versatile design: Supports use across clinical, home, and resource-limited environments with potential for cost-effective manufacturing

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