Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.

Authors

Shangyan Li, Jiqing Huang, Yongyong Yan, Richard T Jaspers, Janak Lal Pathak, Yin Xiao, Qing Zhang

Published in

Bioactive materials. Volume 68. Pages 162-187. Epub Sep 03, 2026.

Abstract

Organoids, as three-dimensional bioactive microtissues capable of recapitulating native organ architecture and function, have become valuable models in biomedical research. However, their broad adoption is constrained by subjective morphological assessment and endpoint assays that compromise standardization and scalability. This review introduces Organoid Intelligent Morphomics (OIM), an integrative analytical framework that synergizes imaging technologies with artificial intelligence to establish a "morphology-function-mechanism" mapping. OIM enables a paradigm shift from qualitative to quantitative analysis, from static to dynamic monitoring, and from superficial observation to deep molecular inference. The applications of OIM are critically examined across three interconnected domains: (1) quality control, encompassing real-time assessment through non-invasive viability quantification and morphological evaluation, as well as predictive quality control for early differentiation outcome forecasting; (2) disease deconstruction, enabling quantitative, multiscale phenotyping of disease morphology and establishing morphology-mechanism association analyses; (3) drug development, facilitating high-throughput efficacy screening, toxicity assessment, and personalized therapeutic guidance. Furthermore, we critically analyze the current data, algorithmic, and translational challenges facing OIM, propose corresponding solutions, and provide a roadmap toward artificial intelligence virtual organoids. Ultimately, OIM holds promise for establishing the critical technological infrastructure for the scalable manufacturing and clinical translation of organoid-based therapeutics.

PMID:
42733575
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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