The sensor patch can be integrated easily with wound dressing and can also be customized for different types and sizes of wounds. Image courtesy of NUS.
A recent invention by a team of researchers from the National University of Singapore (NUS) and A*STAR’s Institute of Materials Research and Engineering, provides a simple, convenient, and effective way of monitoring wound recovery so clinical intervention can be triggered in a timely manner to improve wound care and management.
The PETAL (Paper-like Battery-free In situ AI-enabled Multiplexed) sensor patch comprises 5 colorimetric sensors that can determine the patient’s wound healing status within 15 minutes by measuring a combination of biomarkers—temperature, pH, trimethylamine, uric acid, and moisture of the wound. These biomarkers were carefully selected to effectively assess wound inflammation, infection, as well as the condition of the wound environment. More biomarkers can be added if required.
The sensor patch is able to operate without an energy source—sensor images are captured by a mobile phone and analyzed by artificial intelligence (AI) algorithms to determine the patient’s healing status. In lab experiments, the PETAL sensor patch demonstrated 97% accuracy in differentiating healing and non-healing chronic and burn wounds.
There were no apparent signs of adverse reactions observed on the skin surface in contact with the PETAL sensor patch over 4 days, demonstrating the biocompatibility of the PETAL sensor patch for ambulatory wound monitoring.
This technology can be adapted and customized for other wound types by incorporating different colorimetric sensors, such as glucose, lactate, or Interleukin-6 for diabetic ulcers. The number of detection zones can also be reconfigured to detect different biomarkers concurrently, so its application can be broadened for different wound types.
