Engineers have developed a wearable device called a-Heal that aims to optimise each stage of the wound healing process.
This system takes cues from the body and, with external interventions, enhances the healing process.
Acting as a ‘microscope in a bandage’, the device operates with continuous imaging, allowing AI to identify trends, determine wound healing stages, flag issues and recommend treatments.
The system uses a tiny camera and AI to detect the stage of healing and deliver a treatment in the form of medication or an electric field.
The system responds to the unique healing process of the patient, offering personalised treatment.
The portable, wireless device – from the team at the University of California, Santa Cruz – could make wound therapy more accessible to patients in remote areas or with limited mobility.
Initial preclinical results, published in the journal npj Biomedical Innovations, demonstrate that the device successfully accelerates the healing process.
The team of UC Santa Cruz and UC Davis researchers, sponsored by the DARPA-BETR program and led by UC Santa Cruz Baskin Engineering Endowed Chair and Professor of Electrical and Computer Engineering (ECE) Marco Rolandi, designed the device that combines a camera, bioelectronics and AI for faster wound healing.
The integration in one device makes it a closed-loop system – one of the first of its kind for wound healing as far as the researchers are aware.
The device uses an onboard camera, developed by fellow Associate Professor of ECE Mircea Teodorescu, to take photos of the wound every two hours.
The photos are fed into a machine learning (ML) model, developed by Associate Professor of Applied Mathematics Marcella Gomez, which the researchers refer to as the AI physician, running on a nearby computer.
Teodorescu said: ‘It’s essentially a microscope in a bandage. Individual images say little, but over time, continuous imaging lets AI spot trends, wound healing stages, flag issues, and suggest treatments.’
The AI physician uses the image to diagnose the wound stage and compares that to where the wound should be along a timeline of optimal wound healing.
If the image reveals a lag, the ML model applies a treatment: either medicine, delivered via bioelectronics, or an electric field, which can enhance cell migration toward wound closure.
The device delivers topical fluoxetine, a selective serotonin reuptake inhibitor, which regulates serotonin, reduces inflammation, and promotes wound closure.
The dose, based on preclinical studies by UC Davis's Isseroff, is administered via bioelectronic actuators by Rolandi.
An optimised electric field, developed by UC Davis’s Zhao and Isseroff, also enhances healing.
An AI physician determines the medication dose and electric field strength. After therapy, a camera captures images to restart the process.
The device transmits images and data like healing rate to a secure web interface for manual adjustments by a physician. It attaches to a standard bandage for easy use.
The team is now exploring the potential of this device to enhance healing in chronic and infected wounds.
