Developed by researchers from the National University of Singapore (NUS) and A*STAR’s Institute of Materials Research and Engineering (IMRE), PETAL (Paper-like Battery-free In situ AI-enabled Multiplexed) is claimed to provide a simple, convenient and effective way of monitoring wound recovery.
Currently, wound healing is typically examined visually by a clinician. Wound infections are mostly diagnosed via swabbing followed by a bacteria culture which involves a long waiting time and does not provide timely wound diagnosis. This makes accurate prediction of wound healing challenging in the clinical setting. In addition, wound assessment typically requires frequent manual removal of dressing, which elevates the risks of infection and may cause additional pain for patients.
“To address this challenge, NUS researchers combined our expertise in flexible electronics, artificial intelligence (AI) and sensor data processing with nanosensor capabilities of IMRE researchers to develop an innovative solution that could benefit patients with complex wound conditions,” said Associate Professor Benjamin Tee from the Department of Materials Science and Engineering under the NUS College of Design and Engineering, and the NUS Institute for Health Innovation & Technology.
The PETAL sensor patch comprises five colorimetric sensors that can determine the patient’s wound healing status within 15 minutes by measuring a combination of biomarkers, namely temperature, pH, trimethylamine, uric acid and moisture of the wound.
"We designed the paper-like PETAL sensor patch to be thin, flexible and biocompatible, allowing it to be easily and safely integrated with wound dressing for the detection of biomarkers. We can thus potentially use this convenient sensor patch for prompt, low-cost wound care management at hospitals or even in non-specialist healthcare settings such as homes,” said Dr Su Xiaodi, principal scientist, Soft Materials Department, A*STAR’s IMRE.
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Additionally, the sensor patch can operate without an energy source as sensor images are captured by a mobile phone and analysed by AI algorithms to determine the patient’s healing status.
Assoc Prof Tee said: “Our AI algorithm is capable of rapidly processing data from a digital image of the sensor patch for very accurate classification of healing status. This can be done without removing the sensor from the wound. In this way, doctors and patients can monitor wounds more regularly with little interruption to wound healing. Timely medical intervention can then be administered appropriately to prevent adverse complications and scarring.”
The design and fabrication of the PETAL sensor patch is detailed in Science Advances.
Each PETAL sensor patch consists of a fluidic panel patterned in the form of a five-petal pinwheel flower, with each ‘petal’ acting as a sensing region. An opening in the centre of the fluidic panel collects fluid from the wound and distributes the fluid evenly via five sampling channels to the sensing regions for analysis. Each sensing region uses a different colour-changing chemical to detect and measure the respective wound indicators.
The fluidic panel is sandwiched between two thin films. The top transparent silicone layer allows for normal skin functions of oxygen and moisture exchange, and it also enables image display for accurate image capture and analysis. The bottom wound contact layer attaches the sensor patch to the skin and protects the wound bed from direct contact with the sensor panel.
After enough wound fluid is accumulated (usually within a few hours or over a few days), the PETAL sensor patch completes the detection of biomarkers within 15 minutes. Images or a video of the sensor patch can be recorded on a mobile phone for classification using the proprietary AI algorithm.
In lab experiments, the PETAL sensor patch demonstrated a high accuracy of 97 per cent in differentiating healing and non-healing chronic and burn wounds.
An international patent for this invention has been filed and the researchers plan to advance to human clinical trials.
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