Original title: Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight
Shangyan Li, Jiqing Huang, Yongyong Yan, Richard T Jaspers +3 · September 2026
OrganoidsMiniature 3D versions of organs grown from stem cells that mimic real organ structure and function. are tiny, lab-grown three-dimensional tissues that act like real organs. Scientists study them to understand diseases and test treatments. However, researchers usually judge their shapes by eye or use tests that destroy the tissue, which makes experiments hard to standardize and scale. To solve this, researchers introduced Organoid Intelligent MorphomicsThe comprehensive measurement and analysis of morphological features at scale.. This approach pairs imaging tools with artificial intelligence to map how an organoid looks to how it works and what happens inside its molecules. Instead of static snapshots, computers monitor living tissues continuously and predict cell development early. The system tracks tissue quality without harming the cells, measures disease features at multiple scales, and supports drug testing for efficacy and toxicity. This framework helps turn visual features into biological insights.
Imagine sorting a box of apples by ripeness. Normally you check each apple once and decide. But if the apples keep ripening while you sort, a single check can be wrong — the apple you set aside might be perfect later. In this paper, organoids are the apples, and their appearance changes over time; the framework uses continuous imaging and a computer program to track those changes instead of judging once at the end.
Organoids are used to model diseases and test drugs, but current analysis is subjective and only looks at endpoints. OIM could make organoid analysis quantitative, real-time, and scalable, which matters for drug screening and personalized medicine.
The abstract doesn't say what the specific data, algorithmic, and translational challenges are; it only says they are analyzed and solutions are proposed. It also doesn't provide experimental results or comparisons to existing methods, since it is a review introducing a framework.
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Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight — Shangyan Li, Jiqing Huang, Yongyong Yan et al. — Bioactive materials — doi:10.1016/j.bioactmat.2026.08.043
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.