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16 March 2023 Machine-learning-based analysis of refractive index tomograms to predict the immune status of individual human monocytes (Conference Presentation)
Mahn Jae Lee, Geon Kim, Moosung Lee, Jungwon Shin, Jung Ho Lee, DongHun Ryu, Young Seo Kim, Yoonjae Chung, Kyuseok Kim, YongKeun Park
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Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC123890T (2023) https://doi.org/10.1117/12.2656615
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
Accurate and rapid evaluation of dynamic immune status is critical to determine therapeutic modalities for sepsis patients, which is impeded by the limitations of conventional diagnostic tools. Here, we employ refractive index tomography to quantitatively assess the immune status of human monocytes in a label-free manner. Measurement of refractive index tomograms enabled quantifications of three-dimensional morphological parameters, which revealed a clear increment in lipid droplets content and intracellular inhomogeneities as the septic stage progresses. We leveraged these observations to engineer a deep-learning-based algorithm that predicts the immune status of monocytes, showing over 99 % blind test accuracy.
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Mahn Jae Lee, Geon Kim, Moosung Lee, Jungwon Shin, Jung Ho Lee, DongHun Ryu, Young Seo Kim, Yoonjae Chung, Kyuseok Kim, and YongKeun Park "Machine-learning-based analysis of refractive index tomograms to predict the immune status of individual human monocytes (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC123890T (16 March 2023); https://doi.org/10.1117/12.2656615
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KEYWORDS
Refractive index
3D image processing
3D modeling
Diagnostics
In vitro testing
Phase imaging
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