ArticleKidney research and clinical practice2023
Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells.
Article in Kidney research and clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 37 citations in OpenAlex.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Generation of the functionally enhanced proximal tubular cells from human iPSC-derived kidney organoids.Experimental & molecular medicine · 2026Article
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- Harnessing Cancer Stem Cells and 3D Organoids in Unravelling Spatial and Cellular Heterogeneity in Cancer.International journal of molecular sciences · 2026Review
- Kidney Organoids in Drug Development: Integrating Technological Advances and Standardization for Effective Implementation.Advanced healthcare materials · 2026Review
- Technological Innovations and the Translational Path of Kidney Organoids.Biomedicines · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Patient-derived organoids in functional precision oncology: from experimental models to clinical decision-making.Frontiers in endocrinology · 2026Review
- The 3D landscape of infection: spatial organization andFrontiers in immunology · 2026Review
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Organoid Tracker: A SAM2-Powered Platform for Zero-shot Cyst Analysis in Human Kidney Organoid Videos.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- A deep learning-based computational pipeline predicts developmental outcome in retinal organoids.PLoS biology · 2026Article
- Smarter stem cells: how AI is supercharging iPSC technology.Cell and tissue research · 2025Review
- Immune and Immune-Integrated Organoids as NextGeneration Platforms for Disease Modeling.MedComm · 2025Review
- Multiscale Construction, Evaluation, and Application of Organoids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Gastric Cancer Treatment New Chapter: Organoid Models Leading Personalized Medicine.Molecular diagnosis & therapy · 2025Review
- Prediction of the hypothalamus-pituitary organoid formation using machine learning.Cell reports methods · 2025Article
- Generative Ai for Cardiovascular Cell Type-Specific Fluorescence Colorization of Live-Cell hPSC-Derived Cardiac Organoids.Advanced intelligent discovery · 2025Article
- Cell condensation initiates organogenesis: the role of actin dynamics in supracellular self-organizing process.Cell & bioscience · 2025Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundKidney organoids derived from human pluripotent stem cells (hPSCs) contain multilineage nephrogenic progenitor cells and can recapitulate the development of the kidney. Kidney organoids derived from hPSCs have the potential to be applied in regenerative medicine as well as renal disease modeling, drug screening, and nephrotoxicity testing. Despite biotechnological advances, individual differences in morphological and growth characteristics among kidney organoids need to be addressed before clinical and commercial application. In this study, we hypothesized that an automated noninvasive method based on deep learning of bright-field images of kidney organoids can predict their differentiation status.
methodsBright-field images of kidney organoids were collected on day 18 after differentiation. To train convolutional neural networks (CNNs), we utilized a transfer learning approach. CNNs were trained to predict the differentiation of kidney organoids on bright-field images based on the messenger RNA expression of renal tubular epithelial cells as well as podocytes.
resultsThe best prediction model was DenseNet121 with a total Pearson correlation coefficient score of 0.783 on a test dataset. W classified the kidney organoids into two categories: organoids with above-average gene expression (Positive) and those with below-average gene expression (Negative). Comparing the best-performing CNN with human-based classifiers, the CNN algorithm had a receiver operating characteristic-area under the curve (AUC) score of 0.85, while the experts had an AUC score of 0.48.
conclusionThese results confirmed our original hypothesis and demonstrated that our artificial intelligence algorithm can successfully recognize the differentiation status of kidney organoids.
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