Researchers from the National University of Singapore (NUS) and A*STAR’s Institute of Materials Research and Engineering (IMRE) have recently developed a simple, practical, and efficient method for tracking the healing of wounds, enabling prompt clinical intervention to enhance wound care and management.
Currently, a clinician will usually assess wound healing visually. The most common method for diagnosing wound infections is swabbing, which is followed by a bacteria culture, which requires a lot of waiting time and does not offer prompt wound diagnosis.
Because of this, it can be difficult to predict how a wound will heal in the clinical context. Additionally, frequent manual dressing removal for wound assessment raises the risk of infection and may be painful and traumatic for patients.
The PETAL (Paper-like Battery-free In Situ AI-enabled Multiplexed) sensor patch is made up of five colourimetric sensors that can assess the patient’s wound healing condition in under 15 minutes by detecting a mix of biomarkers such as temperature, pH, trimethylamine, uric acid, and wound wetness. These indicators were carefully chosen to properly detect wound inflammation, infection, and wound environment status.
Each PETAL sensor patch is made up of a fluidic panel that is patterned like a flower with five petals, with each “petal” serving as a sensing zone. The fluidic panel’s central opening collects fluid from the wound and disperses it evenly through five sampling channels to the sensing regions for analysis. Temperature, pH, trimethylamine, uric acid, and moisture are the specific wound indicators that are detected and measured by each sensing location using a separate colour-changing chemical.
A thin film and a fluidic panel are placed together. The top transparent silicone layer for the exchange of oxygen and moisture that occurs naturally in the skin, as well as a picture display for precise image capture and analysis. To minimise wound tissue disruption, the bottom wound contact layer softly adheres the sensor patch to the skin and shields the wound bed from coming into touch with the sensor panel.
The PETAL sensor patch will finish the detection of biomarkers in 15 minutes once enough wound fluid has accumulated (often over the course of a few hours or days). On a mobile phone, images or videos of the sensor patch can be captured for classification using a custom AI algorithm.
The PETAL sensor patch proved to be highly accurate in lab tests, differentiating between chronic and burn wounds that were healing and those that weren’t.
For the treatment and management of wounds, timely and efficient monitoring of the wound-healing condition is essential. Impaired wound healing can lead to life-threatening medical consequences, a significant financial burden on individuals and healthcare systems around the world, and post-burn pathological scars and chronic wounds (those that don’t heal after three months).
The sensor patch can function without a power source since it uses mobile phone cameras to take sensor images, which are then analysed by AI algorithms to evaluate how well the patient is mending.
The PETAL sensor patch is biocompatible for ambulatory wound monitoring as evidenced by the absence of any overtly negative reactions on the skin surface in touch with the patch for four days.
The effectiveness of the PETAL sensor patch was shown in the current investigation on burn wounds and chronic wounds. By adding different colourimetric sensors, such as glucose, lactate, or interleukin-6 for diabetic ulcers, this AI-enabled system can be modified and adjusted for additional wound types. Detecting multiple biomarkers simultaneously is also a simple way to alter the number of detection zones, which broadens the system’s application to a variety of wound types.