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    Organoids are self-organized three-dimensional culture systems and have the advantages of both in vitro and in vivo experiments. However, each organoid has a different degree of self-organization, and methods such as immunofluorescence staining are required for confirmation. Therefore, we established a system to select organoids with high tissue-specific similarity using deep learning without relying on staining by acquiring bright-field images in a non-destructive manner. We identified four biomarkers in RNA extracted from airway organoids. We also predicted biomarker expression by image-based analysis of organoids by convolution neural network, a deep learning method. We predicted airway organoid-specific marker expression from bright-field images of organoids. Organoid differentiation was verified by immunofluorescence staining of the same organoid after predicting biomarker expression in bright-field images. Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening. © 2023. Korean Tissue Engineering and Regenerative Medicine Society.

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    Mi Hyun Lim, Seungmin Shin, Keonhyeok Park, Jaejung Park, Sung Won Kim, Mohammed Abdullah Basurrah, Seungchul Lee, Do Hyun Kim. Deep Learning Model for Predicting Airway Organoid Differentiation. Tissue engineering and regenerative medicine. 2023 Dec;20(7):1109-1117

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    PMID: 37594633

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