Label-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence microscopy and supervised machine learning

We present a novel method that can assay cellular viability in real-time using supervised machine learning and intracellular dynamic activity data that is acquired in a labelfree, non-invasive, and non-destructive manner. Cell viability can be an indicator for cytology, treatment, and diagnosis of diseases. We applied four supervised machine learning models on the observed data and compared the results with a trypan blue assay. The cell death assay performance by the four supervised models had a balanced accuracy of 93.92 +/- 0.86%. Unlike staining techniques, where criteria for determining viability of cells is unclear, cell viability assessment using machine learning could be clearly quantified. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this study, a novel method for assessing cellular viability in real-time using supervised machine learning and non-invasive data acquisition was presented. The performance of the method showed a high accuracy in determining cell death, providing a clear and quantitative assessment compared to conventional staining techniques.