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 label-free, 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.