ArticleAdvanced intelligent discovery2025
Generative Ai for Cardiovascular Cell Type-Specific Fluorescence Colorization of Live-Cell hPSC-Derived Cardiac Organoids.
Article in Advanced intelligent discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multiparametric dynamic optical coherence tomography for evaluation of human cardiac organoids subjected to external stress.Biomedical optics express · 2026Article
- Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.Biotechnology and bioengineering · 2026Article
- A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.bioRxiv : the preprint server for biology · 2026Article
- Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- 3D Cardiac Constructs in Drug Discovery: Current Advances and Future Challenges.Research (Washington, D.C.) · 2026Review
- Artificial intelligence in cardio-oncology: decoding mechanisms, predicting toxicity, and personalizing cancer therapy.Frontiers in cardiovascular medicine · 2026Review
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7 authors.
Funding
Abstract
Human pluripotent stem cell (hPSC)-derived cardiac organoids (COs) are the most recent three-dimensional tissue structure that mimics the human heart's structure and functionality for modeling heart development and disease. Fluorescent labeling and imaging are commonly utilized to characterize the cellular information in COs. However, the additional step of fluorescence labeling and imaging is time-consuming, inefficient, and typically for end-timepoint characterization. Meanwhile, the COs are routinely examined by brightfield/phase contrast microscope to track live-cell organoid formation in structure and morphology. Although the brightfield microscope provides essential information about COs, such as morphology and overall structure, it limits our understanding of cardiovascular cells (e.g., cardiomyocytes, CMs and endothelial cells, ECs) and corresponding quantifications in COs. Is it possible to overcome these limitations of bright-field microscopic imaging and provide cardiovascular cell type-specific information similar to the fluorescence-labeled imaging acquisition in COs? This research addresses this limitation by proposing a generative AI system for colorizing phase contrast images of COs from bright-field microscopic imaging using conditional generative adversarial networks (cGANs) to generate cardiovascular cell type-specific fluorescence images of COs. By giving these phase contrast images with multichannel fluorescence colorization, this intelligence system unlocks cell type and quantifications of COs in high efficiency and accuracy.
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