ArticleTissue engineering and regenerative medicine2023
Deep Learning Model for Predicting Airway Organoid Differentiation.
Article in Tissue engineering and regenerative medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Deep Learning-Based Prediction of Epithelial Cytokine Responses for the Selection of Functionally Consistent Airway Organoids.Biomimetics (Basel, Switzerland) · 2026Article
- Development and validation of deep learning for predicting the growth of ovarian cancer organoids.Chinese medical journal · 2026Article
- Prediction of the hypothalamus-pituitary organoid formation using machine learning.Cell reports methods · 2025Article
- Enhancing Endothelial Differentiation of Mesenchymal Stem Cells Derived from Human Turbinates Using Lab-on-a-Chip Technology.Medicina (Kaunas, Lithuania) · 2025Article
- A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids.Communications biology · 2024Article
- The Development of a Human Respiratory Mucosa-on-a-Chip Using Human Turbinate-Derived Mesenchymal Stem Cells.Medicina (Kaunas, Lithuania) · 2024Article
- Surgical and Regenerative Treatment Options for Empty Nose Syndrome: A Systematic Review.Clinical and experimental otorhinolaryngology · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundOrganoids 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.
methodsWe 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.
resultsWe 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.
conclusionOur study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.