Effective and timely monitoring of the wound-healing process is crucial in wound care and management. Issues with wound healing, like chronic wounds (those that don't heal after three months) and post-burn pathological scars, can lead to life-threatening complications and substantial financial strain on patients and global healthcare systems. Wound healing is traditionally assessed visually by a clinician, and wound infections are mostly identified through swabbing and subsequent bacterial cultures. This method involves extended waiting times and does not provide immediate wound diagnosis, making accurate wound healing predictions difficult in a clinical setting. Furthermore, the frequent removal of dressings for wound assessment increases the risk of infection and can cause additional discomfort and trauma to patients. To overcome this challenge, a research team integrated their expertise in flexible electronics, artificial intelligence (AI), sensor data processing, and nanosensor capabilities to develop an innovative solution that could benefit patients with complicated wound conditions.

A sensor patch invented by a team of researchers from the National University of Singapore (NUS, Singapore) and A*STAR's Institute of Materials Research and Engineering (IMRE, Singapore) offers a simple, efficient, and convenient way to monitor wound healing, thereby facilitating timely clinical intervention to enhance wound care and management. The PETAL (Paper-like Battery-free In situ AI-enabled Multiplexed) sensor patch contains five colorimetric sensors and can assess the patient's wound healing status within 15 minutes by measuring a combination of biomarkers such as temperature, pH, trimethylamine, uric acid, and wound moisture. These biomarkers were specifically chosen to effectively evaluate wound inflammation, infection, and the wound environment's condition.

Unlike the majority of wearable wound sensors that measure only one or a few parameters and require bulky circuit boards and batteries, the PETAL sensor patch currently measures five biomarkers. Additional biomarkers can be included by integrating different colorimetric sensors, such as glucose, lactate, or Interleukin-6 for diabetic ulcers, if needed. The sensor patch does not require a battery and can function without an energy source. Sensor images are captured by a mobile phone and analyzed by AI algorithms to ascertain the patient's healing status. The PETAL sensor patch consists of a fluidic panel designed like a five-petal pinwheel flower, with each 'petal' serving as a sensing region. An opening in the fluidic panel's center collects wound fluid and distributes it evenly through five sampling channels to the sensing regions for analysis. Each sensing region uses a different color-changing chemical to detect and measure respective wound indicators like temperature, pH, trimethylamine, uric acid, and moisture.

A fluidic panel sandwiched between two thin films comprises a top transparent silicone layer for facilitating normal skin functions of oxygen and moisture exchange and enables image display for accurate image capture and analysis. The bottom layer, in contact with the wound, gently affixes the sensor patch to the skin, protecting the wound bed from direct contact with the sensor panel to minimize disruptions to wound tissue. Once sufficient wound fluid is collected, the PETAL sensor patch can detect biomarkers within 15 minutes. Images or a video of the sensor patch can be recorded on a mobile phone for classification using a proprietary AI algorithm. Lab experiments have demonstrated a high accuracy rate of 97% of the PETAL sensor patch in differentiating between healing and non-healing chronic and burn wounds. There were no visible signs of adverse reactions observed on the skin surface in contact with the PETAL sensor patch over four days, attesting to the biocompatibility of the PETAL sensor patch for outpatient wound monitoring.

"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,” explained Dr. Su Xiaodi, Principal Scientist, Soft Materials Department, A*STAR's IMRE. “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."

"Our AI algorithm is capable of rapidly processing data from a digital image of the sensor patch for very accurate classification of healing status,” said NUS Associate Professor Benjamin Tee. “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." 

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