Battery-free and AI-enabled multiplexed sensor patches for wound monitoring
- June 16, 2023
Xin Ting Zheng, Zijie Yang2, Laura Sutarlie, Moogaambikai Thangaveloo, Yong Yu, Nur Asinah Binte Mohamed Salleh, Jiah Shin Chin, Ze Xiong, David Lawrence Becker, Xian Jun Loh, Benjamin C. K. Tee, Xiaodi Su
Abstract
Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We report a paper-like battery-free in situ AI-enabled multiplexed (PETAL) sensor for holistic wound assessment by leveraging deep learning algorithms. This sensor consists 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 can 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 is demonstrated. This PETAL sensor allows early warning of adverse events, which could trigger immediate clinical intervention to facilitate wound care management.
Link [Battery-free and AI-enabled multiplexed sensor patches for wound monitoring]
