Towards adaptive bioelectronic wound therapy with integrated real-time diagnostics and machine learning–driven closed-loop control
Year: 2025
Type: article
Abstract: Impaired wound healing affects millions worldwide, especially those without timely healthcare access. Here, we have developed a portable and wireless platform for real-time, continuous, and adaptive bioelectronic wound therapy (a-Heal). The platform integrates a wearable device for wound imaging and delivery of therapy with an ML Physician. The ML Physician analyzes wound images, diagnoses the wound stage, and prescribes therapies to guide optimal healing. Bioelectronic actuators in the wearable device deliver therapies, including electric fields or drugs, dynamically in a closed-loop system. a-Heal evaluates wound progress, adapts therapy as needed, and sends updates to human physicians through a graphical user interface, which also supports manual intervention. In preliminary studies using a large animal model, a-Heal promoted tissue regeneration, reduced inflammation, and accelerated healing, highlighting its potential in personalized wound care.
Source: npj Biomedical Innovations.
Authors: Houpu Li, Hsin-ya Yang, Fan Lu, Wan Shen Hee, Narges Asefifeyzabadi, Prabhat Baniya, Anthony Gallegos, Kaelan Schorger, Kan Zhu, Cynthia Recendez, Maryam Tebyani, Manasa Kesapragada, Gordon Keller, Sujung Kim, George Luka, Ksenia Zlobina, Tiffany Nguyen, Sydnie Figuerres, Celeste Franco, Koushik Devarajan, Alexie Barbee, Kylie Lin, Shannon M. Clayton, Annabelle Eaton, Elham Aslankoohi, Athena M. Soulika, Min Zhao, Mircea Teodorescu, Marcella Gomez, Roslyn Rivkah Isseroff, Marco Rolandi
Language: English
Topic: Wound Healing and Treatments
Subfield: Rehabilitation
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G1137031328, G7801537192, G7856428000
OpenAlex ID: W4414442684