ArticleThe Journal of clinical investigation2020
Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.
Article in The Journal of clinical investigation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 76 citations in OpenAlex.
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- AI driven 3D subcellular RPE map discovers cell state transitions in establishment of apical-basal polarity.NPJ artificial intelligence · 2026Article
- Genetic removal of Nlrp3 protects against age-related and R345W Efemp1-induced basal laminar deposit formation.Cell death & disease · 2025Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Early and non-destructive prediction of the differentiation efficiency of human induced pluripotent stem cells using imaging and machine learning.Scientific reports · 2025Article
- Molecular Determinants of the Human Retinal Pigment Epithelium Cell Fate and Potential Pharmacogenomic Targets for Precision Medicine.International journal of molecular sciences · 2025Article
- Advantages and challenges of using allogeneic vs. autologous sources for neuronal cell replacement in Parkinson's disease: Insights from non-human primate studies.Brain research bulletin · 2025Review
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Regenerative rehabilitation: a novel multidisciplinary field to maximize patient outcomes.Medical review (2021) · 2024Review
- Tissue Engineering of Outer Blood Retina Barrier for Therapeutic Development.Current opinion in biomedical engineering · 2024Article
- A literature review: machine learning-based stem cell investigation.Annals of translational medicine · 2024Review
- Recent Progress in Retinal Pigment Epithelium Cell-Based Therapy for Retinal Disease.Stem cells translational medicine · 2024Review
- Considerations for Developing an Autologous Induced Pluripotent Stem Cell (iPSC)-Derived Retinal Pigment Epithelium (RPE) Replacement Therapy.Cold Spring Harbor perspectives in medicine · 2024Review
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development.Frontiers in cell and developmental biology · 2024Review
- A biologically validated mathematical model for decoding epithelial apical, basolateral, and paracellular electrical properties.American journal of physiology. Cell physiology · 2023Article
- Deep Learning Model for Predicting Airway Organoid Differentiation.Tissue engineering and regenerative medicine · 2023Article
- Exploring the promising potential of induced pluripotent stem cells in cancer research and therapy.Molecular cancer · 2023Review
- Medium Depth Influences O2 Availability and Metabolism in Human RPE Cultures.Investigative ophthalmology & visual science · 2023Article
- Propensity of Patient-Derived iPSCs for Retinal Differentiation: Implications for Autologous Cell Replacement.Stem cells translational medicine · 2023Article
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to noninvasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell-derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine-learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate noninvasive cell therapy characterization can be achieved with QBAM and machine learning.
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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.