Caltech’s pioneering iCares smart bandage successfully monitors chronic wounds in human patients, offering real-time data and advancing healing through innovative microfluidic technology.
In a breakthrough development poised to transform wound care, a team of Caltech researchers led by Wei Gao, a professor of medical engineering, has successfully demonstrated the efficacy of their cutting-edge smart bandage, iCares, in monitoring chronic wounds in human patients.
This innovative bandage, described as a “lab on skin” by Gao, promises not only to monitor chronic wounds but also to deliver targeted treatments, speeding up the healing process for injuries that otherwise heal slowly, such as those caused by diabetes or poor blood circulation.
In a recent milestone, Gao’s team, in collaboration with the Keck School of Medicine of USC, showcased iCares’ ability to continually sample wound fluid from 20 human patients. This fluid sampling is crucial as it carries biomarkers that reflect the body’s inflammatory response.
“Our innovative microfluidics remove moisture from the wound, which helps with healing. They also make sure that samples analyzed by the bandage are fresh, not a mixture of old and new fluid. To get accurate measurements, we need to sample only the newest fluid at a wound site,” Gao said in a news release, highlighting the precision and efficiency of the device. “In this way, iCares can watch in real time for important biomarkers of inflammation and infection.”
The bandage consists of a flexible, biocompatible polymer strip that is inexpensive to 3D print. It’s equipped with three microfluidic modules to manage and analyze wound exudate. One module draws excess moisture from the wound, another channels this fluid to a sensor array for analysis, and a third one carries the sampled fluid outside the bandage for disposal.
Caption: An iCares smart bandage
Credit: Caltech
Crucially, iCares can detect inflammation and infection signals, such as nitric oxide and hydrogen peroxide, potentially one to three days before patients show any symptoms. This early detection is crucial for preventing complications and ensuring timely interventions.
Additionally, a machine-learning algorithm integrated into iCares further enhances its capabilities by classifying wound types and predicting healing times with accuracy on par with expert clinicians.
The research is published in the journal Science Translational Medicine.