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We introduce cell dynamic activity analysis method-based combination of dynamic full-field optical coherence tomography (DFFOCT) and machine learning (ML) models. DFFOCT can monitor intracellular migration label-free by capturing scatters movement inside of cells. Since ML builds classification criteria through learning a lot of data, based on the intracellular scatter migration observed through DFFOCT, it is possible to judge abnormal signs of cells regardless of changes in the external experimental environment. We compared the suggested analysis method and staining analysis method for the change of state of HeLa cells (including cell data) and verified the validity.
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Soongho Park, Thien Nguyen, Vinay Veluvolu, Jinho Park, Amir Gandjbakhche, "Evaluation of cell dynamic activity using machine learning and intracellular migration observations (Conference Presentation)," Proc. SPIE PC12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023, PC1239114 (16 March 2023); https://doi.org/10.1117/12.2660490