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

Soongho Park; Vinay Veluvolu; William S Martin; Thien Nguyen; Jinho Park; Dan L Sackett; Claude Boccara; Amir Gandjbakhche

doi:10.1364/BOE.452471

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

Biomed Opt Express. 2022 May 5;13(6):3187-3194. doi: 10.1364/BOE.452471. eCollection 2022 Jun 1.

Authors

Soongho Park¹, Vinay Veluvolu¹, William S Martin¹, Thien Nguyen¹, Jinho Park¹, Dan L Sackett¹, Claude Boccara², Amir Gandjbakhche¹

Affiliations

¹ Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 49 Convent Dr., Bethesda, MD 20814, USA.
² Institut Langevin, ESPCI Paris, CNRS, PSL University, 1 rue Jussieu, 75005 Paris, France.

Abstract

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. 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.