Tissue Eng Regen Med.  2023 Dec;20(7):1109-1117. 10.1007/s13770-023-00563-8.

Deep Learning Model for Predicting Airway Organoid Differentiation

• Lim MH
• Shin S
• Park K
• Park J
• Kim SW
• Basurrah MA
• Lee S
• Kim DH

• KMID: 2548531
• DOI: http://doi.org/10.1007/s13770-023-00563-8

Abstract

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 nondestructive 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.

Keyword

Airway organoid; Bright-field image; Deep learning