Open AccessLabel-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence microscopy and supervised machine learning
Author(s) -
Soongho Park,
Vinay Veluvolu,
William H. Martin,
Thien Nguyen,
Jinho Park,
Dan L. Sackett,
Claude Boccara,
Amir Gandjbakhche
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.452471
Subject(s) - viability assay , optical coherence tomography , computer science , machine learning , trypan blue , artificial intelligence , cell , medicine , biology , biochemistry , radiology
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.