Deep Learning Model for Predicting Airway Organoid Differentiation.

Background: 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.

Methods: 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.

Results: 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.

Conclusion: Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.