Duke-Nokia AI tracks wound healing

This collaboration applies telecommunications-grade hardware to a critical medical diagnostic gap, solving the problem of wounds that appear healthy on the surface while failing to regenerate underneath.

• –Deep learning automation eliminates the human subjectivity and high variance found in traditional clinical wound staging.
• –Real-time tracking of sub-surface vascular dynamics provides a new quantitative benchmark for evaluating the effectiveness of regenerative biomaterials.
• –The system's ability to identify optimal mechanical properties for hydrogels demonstrates the power of AI-driven feedback in material science and therapy development.
• –Transitioning high-resolution OCT from ophthalmology to wound care signals a broader move toward portable, high-precision diagnostic tools for bedside use.
• –The platform’s predictive insights could significantly reduce complications for chronic conditions like diabetic foot ulcers.