ArticleCell reports methods2024
Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- Translating Pharmacology from Animal Models to Organoids: Advancing Predictive Human Biology in Drug Discovery.Stem cell reviews and reports · 2026Review
- Organoids in drug development: from predictive models to regulatory integration.Drug discovery today · 2026Review
- From organoid culture to manufacturing: technologies for reproducible and scalable organoid production.npj biomedical innovations · 2026Review
- Artificial intelligence in cardio-oncology: decoding mechanisms, predicting toxicity, and personalizing cancer therapy.Frontiers in cardiovascular medicine · 2026Review
- 3D Cardiac Constructs in Drug Discovery: Current Advances and Future Challenges.Research (Washington, D.C.) · 2026Review
- Integrating Bioprinting and Increased Throughput: Next-Generation Models for Cardiac Research.International journal of molecular sciences · 2025Review
- Mechanobiological engineering strategies for organoid culture.APL bioengineering · 2025Review
- Effects of PDMS culture on stem cell differentiation towards definitive endoderm and hepatocytes.Acta biomaterialia · 2025Article
- Advances in cardiac organoid research: implications for cardiovascular disease treatment.Cardiovascular diabetology · 2025Review
- Organoids: development and applications in disease models, drug discovery, precision medicine, and regenerative medicine.MedComm · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.
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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.