2023-06-26 シンガポール国立大学(NUS)
◆この薄く柔軟なパッチは、異なる創傷タイプにも適用できます。国際特許が出願され、次は人間の臨床試験に進む予定です。
<関連情報>
- https://news.nus.edu.sg/innovative-paper-like-battery-free-ai-enabled-sensor-for-holistic-wound-monitoring/
- https://www.science.org/doi/10.1126/sciadv.adg6670
創傷モニタリングのための電池不要でAI対応の多重化センサーパッチ Battery-free and AI-enabled multiplexed sensor patches for wound monitoring
Xin Ting Zheng,Zijie Yang,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, and Xiaodi Su
Science Advances Published:16 Jun 2023
DOI:https://doi.org/10.1126/sciadv.adg6670
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.
