Nanowerk June 26, 2023
Researchers in Singapore developed a paper-like battery-free in situ AI-enabled multiplexed (PETAL) sensor for holistic wound assessment by leveraging deep learning algorithms. This sensor consisted of a wax-printed paper panel with five colorimetric sensors for temperature, pH, trimethylamine, uric acid, and moisture. Sensor images captured by a mobile phone were analyzed by neural network–based machine learning algorithms to determine healing status. For ex situ detection via exudates collected from rat perturbed wounds and burn wounds, the PETAL sensor could classify healing versus nonhealing status with an accuracy as high as 97%. With the sensor patches attached on rat burn wound models, in situ monitoring of wound progression or severity was demonstrated. The PETAL sensor allowed early warning of adverse events, which could trigger immediate clinical intervention to facilitate wound care management… read more. Open Access TECHNICAL ARTICLE
Schematic of a battery-free colorimetric multiplexed sensor for… Credit: SCIENCE ADVANCES, 16 Jun 2023, Vol 9, Issue 24