Prof. Guo Shifeng’s team at the Shenzhen Institute of Advanced Technology (SIAT) of the Chinese Academy of Sciences has proposed a novel method that fills the gap between physical measurement and artificial intelligence in bacterial viability detection. The study was published in Cell Reports Physical Science.
Novel method uses nanomechanics and machine learning for rapid bacterial viability detection
