{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T19:02:42Z","timestamp":1789671762682,"version":"4.0.1"},"reference-count":220,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Bioactive Materials"],"published-print":{"date-parts":[[2027,2]]},"DOI":"10.1016\/j.bioactmat.2026.08.043","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:07:02Z","timestamp":1788448022000},"page":"162-187","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight"],"prefix":"10.1016","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4404-3598","authenticated-orcid":false,"given":"Shangyan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiqing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8070-6116","authenticated-orcid":false,"given":"Yongyong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6951-0952","authenticated-orcid":false,"given":"Richard T.","family":"Jaspers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janak Lal","family":"Pathak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4272-0818","authenticated-orcid":false,"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bioactmat.2026.08.043_bib1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1038\/s43586-022-00174-y","article-title":"Organoids","volume":"2","author":"Zhao","year":"2022","journal-title":"Nat. Rev. Methods Primers"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s42242-022-00226-y","article-title":"Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence","volume":"6","author":"Du","year":"2023","journal-title":"BioDes. Manuf."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib3","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/ctm2.1499","article-title":"Organoid assessment technologies","volume":"13","author":"Gu","year":"2023","journal-title":"Clin. Transl. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib4","doi-asserted-by":"crossref","first-page":"736","DOI":"10.3390\/cancers13040736","article-title":"Neuroblastoma invasion strategies are regulated by the extracellular matrix","volume":"13","author":"Gavin","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib5","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1093\/annonc\/mdx034","article-title":"Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology","volume":"28","author":"Limkin","year":"2017","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib6","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1007\/s43657-021-00031-0","article-title":"Mapping cell phenomics with multiparametric flow cytometry assays","volume":"2","author":"Liu","year":"2022","journal-title":"Phenomics (Cham, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib7","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1111\/joa.12287","article-title":"From gross anatomy to the nanomorphome: stereological tools provide a paradigm for advancing research in quantitative morphomics","volume":"226","author":"Mayhew","year":"2015","journal-title":"J. Anat."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1063\/5.0039639","article-title":"Self-aligned myofibers in 3D bioprinted extracellular matrix-based construct accelerate skeletal muscle function restoration","volume":"8","author":"Lee","year":"2021","journal-title":"Appl. Phys. Rev."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2024.102842","article-title":"Artificial intelligence for high content imaging in drug discovery","volume":"87","author":"Carreras-Puigvert","year":"2024","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2024.111434","article-title":"A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens","volume":"27","author":"Comolet","year":"2024","journal-title":"iScience"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib11","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1038\/s41591-024-03489-3","article-title":"Clinical applications of human organoids","volume":"31","author":"Verstegen","year":"2025","journal-title":"Nat. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib12","first-page":"5","article-title":"hiPSC-Driven organoid construction and application prospects","volume":"2","author":"Liu","year":"2025","journal-title":"Regen. Med. Dentist."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib13","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1145\/3448250","article-title":"Deep learning for AI","volume":"64","author":"Bengio","year":"2021","journal-title":"Commun. ACM"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib14","article-title":"What is machine learning, artificial neural networks and deep learning?-examples of practical applications in medicine","volume":"13","author":"Kufel","year":"2023","journal-title":"Diagnostics (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.dtrs.2025.100005","article-title":"The role of artificial intelligence in shaping dentistry through advancement in data acquisition, clinical practice, education, and research","volume":"1","author":"Tay","year":"2026","journal-title":"Dental Research"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib16","article-title":"Development of a deep learning based image processing tool for enhanced organoid analysis","volume":"13","author":"Park","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10462-023-10631-z","article-title":"Deep learning models for digital image processing: a review","volume":"57","author":"Archana","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib18","first-page":"121","article-title":"Present application and perspectives of organoid imaging technology","volume":"9","author":"Fei","year":"2022","journal-title":"Bioengineering (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib19","article-title":"High-definition DIC imaging uncovers transient stages of pathogen infection cycles on the surface of human adult stem cell-derived intestinal epithelium","volume":"13","author":"van Rijn","year":"2021","journal-title":"mBio"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib20","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/mam\/ozae119","article-title":"Structural analysis of cerebral organoids using confocal microscopy and transmission\/scanning electron microscopy","volume":"31","author":"Noh","year":"2025","journal-title":"Microsc. Microanal. : Off. J. Microscopy Soc. Am. Microbeam Anal. So. Microsc. Soc. Canada"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib21","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1038\/s41596-019-0160-8","article-title":"High-resolution 3D imaging of fixed and cleared organoids","volume":"14","author":"Dekkers","year":"2019","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.bios.2022.114136","article-title":"Longitudinal morphological and functional characterization of human heart organoids using optical coherence tomography","volume":"207","author":"Ming","year":"2022","journal-title":"Biosens. Bioelectron."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib23","article-title":"Label-free 3D photoacoustic imaging of tumor organoids for volumetric drug screening","volume":"12","author":"Luo","year":"2025","journal-title":"Advanced science (Weinheim, Baden-Wurttemberg, Germany)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib24","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adr0654","article-title":"Axially multifocal metalens for 3D volumetric photoacoustic imaging of neuromelanin in live brain organoid","volume":"11","author":"Barulin","year":"2025","journal-title":"Sci. Adv."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib25","doi-asserted-by":"crossref","DOI":"10.3791\/68529","article-title":"Label-free, high-resolution 3D imaging and machine learning analysis of intestinal organoids via low-coherence holotomography","author":"Cho","year":"2025","journal-title":"J. Vis. Exp."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib26","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2024.1360482","article-title":"Applications of multiphoton microscopy in imaging cerebral and retinal organoids","volume":"18","author":"Lacin","year":"2024","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib27","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/s43586-021-00069-4","article-title":"Light sheet fluorescence microscopy","volume":"1","author":"Stelzer","year":"2021","journal-title":"Nat. Rev. Methods Primers"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib28","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1038\/s41551-024-01249-9","article-title":"Low-cost and scalable projected light-sheet microscopy for the high-resolution imaging of cleared tissue and living samples","volume":"8","author":"Chen","year":"2024","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib29","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1038\/s41566-025-01659-4","article-title":"Curved light sheet microscopy for centimetre-scale cleared tissue imaging","volume":"19","author":"Tang","year":"2025","journal-title":"Nat. Photonics"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib30","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1038\/s41377-025-01895-x","article-title":"Multi-photon, label-free photoacoustic and optical imaging of NADH in brain cells","volume":"14","author":"Osaki","year":"2025","journal-title":"Light Sci. Appl."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib31","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41377-025-02177-2","article-title":"Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis","volume":"15","author":"Deloria","year":"2026","journal-title":"Light Sci. Appl."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.xpro.2026.104423","article-title":"Protocol for quality control screening of brain organoid morphology","volume":"7","author":"Chiaradia","year":"2026","journal-title":"STAR Protoc."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib33","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010584","article-title":"OrganoID: a versatile deep learning platform for tracking and analysis of single-organoid dynamics","volume":"18","author":"Matthews","year":"2022","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2024.115237","article-title":"Advanced 3D imaging and organoid bioprinting for biomedical research and therapeutic applications","volume":"208","author":"Maharjan","year":"2024","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib35","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-72038-2","article-title":"Non-invasive label-free imaging analysis pipeline for in situ characterization of 3D brain organoids","volume":"14","author":"Serafini","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib36","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-78130-7","article-title":"Multiscale 3D phenotyping of human cerebral organoids","volume":"10","author":"Albanese","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib37","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1158\/0008-5472.CAN-23-1100","article-title":"Deep learning-based 3D single-cell imaging analysis pipeline enables quantification of cell-cell interaction dynamics in the tumor microenvironment","volume":"84","author":"Liu","year":"2024","journal-title":"Cancer Res."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib38","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1038\/s41598-024-53323-6","article-title":"Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction","volume":"14","author":"Li","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib39","doi-asserted-by":"crossref","DOI":"10.1002\/bies.202300114","article-title":"From pixels to insights: machine learning and deep learning for bioimage analysis","volume":"46","author":"Jan","year":"2024","journal-title":"Bioessays"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib40","doi-asserted-by":"crossref","first-page":"5319","DOI":"10.1038\/s41598-017-18815-8","article-title":"Automated brightfield morphometry of 3D organoid populations by OrganoSeg","volume":"8","author":"Borten","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib41","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.3390\/app9081616","article-title":"Open-source tools for volume estimation of 3D multicellular aggregates","volume":"9","author":"De Santis","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib42","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-48874-y","article-title":"OrgaQuant: human intestinal organoid localization and quantification using deep convolutional neural networks","volume":"9","author":"Kassis","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib43","first-page":"2948","article-title":"3D-Cell-Annotator: an open-source active surface tool for single-cell segmentation in 3D microscopy images","volume":"36","author":"Tasnadi","year":"2020","journal-title":"Bioinformatics (Oxford, England)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib44","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.slasd.2022.03.004","article-title":"deepOrganoid: a brightfield cell viability model for screening matrix-embedded organoids","volume":"27","author":"Powell","year":"2022","journal-title":"Slas Discovery"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib45","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.771173","article-title":"Imaging-based machine learning analysis of patient-derived tumor organoid drug response","volume":"11","author":"Spiller","year":"2021","journal-title":"Front. Oncol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib46","article-title":"CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia","volume":"3","author":"Andr\u00e9s-San Rom\u00e1n","year":"2023","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib47","article-title":"VONet: a deep learning network for 3D reconstruction of organoid structures with a minimal number of confocal images","volume":"5","author":"Song","year":"2024","journal-title":"Patterns (N Y)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib48","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1038\/s41592-025-02685-4","article-title":"Digitalized organoids: integrated pipeline for high-speed 3D analysis of organoid structures using multilevel segmentation and cellular topology","volume":"22","author":"Ong","year":"2025","journal-title":"Nat. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib49","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1111\/jmi.12520","article-title":"Quantification of substrate and cellular strains in stretchable 3D cell cultures: an experimental and computational framework","volume":"266","author":"Gonz\u00e1lez-Avalos","year":"2017","journal-title":"J. Microsc."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib50","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.chembiol.2018.01.015","article-title":"Repurposing high-throughput image assays enables biological activity prediction for drug discovery","volume":"25","author":"Simm","year":"2018","journal-title":"Cell Chem. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib51","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1013589","article-title":"A semi-automated algorithm for image analysis of respiratory organoids","volume":"21","author":"Demchenko","year":"2025","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib52","doi-asserted-by":"crossref","first-page":"R231","DOI":"10.1016\/j.cub.2019.02.034","article-title":"Neural network models and deep learning","volume":"29","author":"Kriegeskorte","year":"2019","journal-title":"Curr. Biol. : CB"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib53","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008630","article-title":"Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning","volume":"17","author":"Mergenthaler","year":"2021","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib54","doi-asserted-by":"crossref","first-page":"171","DOI":"10.34133\/bmr.0171","article-title":"A new perspective on precision medicine: the power of digital organoids","volume":"29","author":"Yang","year":"2025","journal-title":"Biomater. Res."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib55","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1220172","article-title":"Brain organoid data synthesis and evaluation","volume":"17","author":"Br\u00e9mond-Martin","year":"2023","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib56","article-title":"Generative frame interpolation enhances tracking of biological objects in time-lapse microscopy","author":"Kaondal","year":"2025","journal-title":"bioRxiv"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib57","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: the success of U-Net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib58","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compbiomed.2023.107840","article-title":"Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation","volume":"169","author":"Jiao","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib59","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1039\/c3ib20275e","article-title":"Rapid and automated multidimensional fluorescence microscopy profiling of 3D human breast cultures","volume":"5","author":"Park","year":"2013","journal-title":"Integr. Biol. : Quant. Biosci. Nano Macro"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib60","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1007\/s10439-014-1223-1","article-title":"Bioengineered scaffolds for 3D analysis of glioblastoma proliferation and invasion","volume":"43","author":"Heffernan","year":"2015","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib61","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.jcmgh.2018.05.004","article-title":"A high-throughput organoid microinjection platform to study gastrointestinal microbiota and luminal physiology","volume":"6","author":"Williamson","year":"2018","journal-title":"Cell. Mol. Gastroenterol. Hepatol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib62","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2026.109824","article-title":"Development of deep learning \u201cDTONet\u201d for accurate detection of tumor organoids","volume":"120","author":"Li","year":"2026","journal-title":"Biomed. Signal Process Control"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib63","doi-asserted-by":"crossref","first-page":"8377","DOI":"10.3390\/app15158377","article-title":"Orga-Dete: an improved lightweight deep learning model for lung organoid detection and classification","volume":"15","author":"Huang","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib64","doi-asserted-by":"crossref","first-page":"5040","DOI":"10.1038\/s41598-025-89409-y","article-title":"Deliod a lightweight detection model for intestinal organoids based on deep learning","volume":"15","author":"Sun","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib65","article-title":"IDP-Head: an interactive dual-perception architecture for organoid detection in mouse microscopic images","volume":"10","author":"Yang","year":"2025","journal-title":"Biomimetics (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib66","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107847","article-title":"Deep-Orga: an improved deep learning-based lightweight model for intestinal organoid detection","volume":"169","author":"Leng","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib67","first-page":"3084","article-title":"An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models","volume":"37","author":"Winkelmaier","year":"2021","journal-title":"Bioinformatics (Oxford, England)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib68","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.3390\/biomedicines11102687","article-title":"Mu-Net a light architecture for small dataset segmentation of brain organoid bright-field images","volume":"11","author":"Br\u00e9mond Martin","year":"2023","journal-title":"Biomedicines"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib69","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1016\/j.bpj.2023.03.038","article-title":"Active mesh and neural network pipeline for cell aggregate segmentation","volume":"122","author":"Smith","year":"2023","journal-title":"Biophys. J."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib70","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1109\/JBHI.2024.3511422","article-title":"3D-Organoid-SwinNet: high-content profiling of 3D organoids","volume":"29","author":"Sohaib","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib71","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1109\/JAS.2023.124026","article-title":"Multi-Axis attention with convolution parallel block for organoid segmentation","volume":"11","author":"Hu","year":"2024","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib72","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123888","article-title":"A local\u2013global unified scheme driven by positionable texture and multi-level boundary for lung cancer organoids segmentation","volume":"250","author":"Fan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib73","first-page":"1216","article-title":"Enhanced U-Net-Based deep learning model for automated segmentation of organoid images","volume":"12","author":"Alani","year":"2025","journal-title":"Bioengineering (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib74","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giaf027","article-title":"Image segmentation of treated and untreated tumor spheroids by fully convolutional networks","volume":"14","author":"Streller","year":"2025","journal-title":"GigaScience"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib75","doi-asserted-by":"crossref","first-page":"7928","DOI":"10.1038\/s41598-026-37526-7","article-title":"Fast learning-free organoid quantification and tracking with OrganoSeg2","volume":"16","author":"Wells","year":"2026","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib76","doi-asserted-by":"crossref","first-page":"46117","DOI":"10.1021\/acsomega.4c06450","article-title":"OrganoLabeler: a quick and accurate annotation tool for organoid images","volume":"9","author":"Kahveci","year":"2024","journal-title":"ACS Omega"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib77","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011386","article-title":"In-silico and in-vitro morphometric analysis of intestinal organoids","volume":"19","author":"Montes-Olivas","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib78","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s40747-025-02177-0","article-title":"Organfit: a multi-scale convolutional model with ellipse fitting for organoid identification","volume":"12","author":"Tong","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib79","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/TFUZZ.2025.3622935","article-title":"Fuzzy mixture-of-experts aggregation for organoid identification with multiscale state space features","volume":"34","author":"Deng","year":"2026","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib80","doi-asserted-by":"crossref","DOI":"10.1242\/dev.199611","article-title":"MOrgAna: accessible quantitative analysis of organoids with machine learning","volume":"148","author":"Gritti","year":"2021","journal-title":"Development"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib81","article-title":"A convexity-preserving level-set method for the segmentation of tumor organoids","volume":"11","author":"Lei","year":"2024","journal-title":"Bioengineering (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib82","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1002\/cyto.a.24514","article-title":"Reliable identification and quantification of neural cells in microscopic images of neurospheres","volume":"101","author":"Foerster","year":"2022","journal-title":"Cytometry A"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib83","doi-asserted-by":"crossref","first-page":"5548","DOI":"10.1038\/s41598-024-82639-6","article-title":"Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images","volume":"15","author":"Riendeau","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib84","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1038\/s41597-025-04700-x","article-title":"Inner ear multiple primary cell type detection system","volume":"12","author":"Li","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib85","article-title":"OSCAR is an online ML-powered tool for organoid cell counting using bright-field images","volume":"5","author":"Burnell","year":"2025","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib86","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s42003-024-05966-4","article-title":"OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion","volume":"7","author":"Lefferts","year":"2024","journal-title":"Commun. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib87","doi-asserted-by":"crossref","DOI":"10.1111\/gtc.70055","article-title":"Quantitative imaging analysis of internal structural complexity in mouse heart organoids; comparison to mouse embryonic hearts","volume":"30","author":"Kaneko","year":"2025","journal-title":"Genes Cells : Devoted Mol. Cell. Mech."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib88","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1364\/BOE.515781","article-title":"Deep learning based characterization of human organoids using optical coherence tomography","volume":"15","author":"Wang","year":"2024","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib89","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2022.1080273","article-title":"A novel deep learning segmentation model for organoid-based drug screening","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib90","article-title":"A deep learning model for drug screening and evaluation in bladder cancer organoids","volume":"13","author":"Zhang","year":"2023","journal-title":"Front. Oncol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib91","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1089\/ten.tec.2023.0150","article-title":"Organalysis: multifunctional image preprocessing and analysis software for cardiac organoid studies","volume":"29","author":"Singaraju","year":"2023","journal-title":"Tissue Eng. C Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib92","doi-asserted-by":"crossref","first-page":"8406","DOI":"10.1038\/s41467-023-44162-6","article-title":"High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos","volume":"14","author":"Mukashyaka","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib93","article-title":"Organoid morphology-guided classification for oral cancer reveals prognosis","volume":"6","author":"Lee","year":"2025","journal-title":"Cell Rep. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib94","doi-asserted-by":"crossref","DOI":"10.1002\/smtd.202401050","article-title":"3D quantification of viral transduction efficiency in living human retinal organoids","volume":"10","author":"Rogler","year":"2026","journal-title":"Small Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib95","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3389\/fgene.2020.00248","article-title":"Application and comparison of supervised learning strategies to classify polarity of epithelial cell spheroids in 3D culture","volume":"11","author":"Soetje","year":"2020","journal-title":"Front. Genet."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib96","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1012256","article-title":"Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework","volume":"20","author":"Gunnarsson","year":"2024","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib97","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s13402-024-00958-2","article-title":"OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data","volume":"48","author":"Ferreira","year":"2025","journal-title":"Cell. Oncol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib98","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41392-022-01024-9","article-title":"Human organoids in basic research and clinical applications","volume":"7","author":"Tang","year":"2022","journal-title":"Signal Transduct. Targeted Ther."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib99","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1364\/OPTICA.2.000877","article-title":"Inverse-power-law behavior of cellular motility reveals stromal-epithelial cell interactions in 3D co-culture by OCT fluctuation spectroscopy","volume":"2","author":"Oldenburg","year":"2015","journal-title":"Optica"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib100","doi-asserted-by":"crossref","first-page":"3789","DOI":"10.1364\/BOE.428197","article-title":"Volumetric growth tracking of patient-derived cancer organoids using optical coherence tomography","volume":"12","author":"Gil","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib101","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s13402-022-00750-0","article-title":"OrBITS: label-free and time-lapse monitoring of patient derived organoids for advanced drug screening","volume":"46","author":"Deben","year":"2023","journal-title":"Cell. Oncol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib102","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1186\/s12915-025-02411-8","article-title":"A knowledge-driven deep learning framework for organoid morphological segmentation and characterization","volume":"23","author":"Qin","year":"2025","journal-title":"BMC Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib103","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104490","article-title":"A deep learning model for detection and tracking in high-throughput images of organoid","volume":"134","author":"Bian","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib104","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics14121217","article-title":"Segmentation and multi-timepoint tracking of 3D cancer organoids from optical coherence tomography images using deep neural networks","volume":"14","author":"Branciforti","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib105","article-title":"A biomimetic physical-biological coupled bone organoid 3D culture system for bone repair","volume":"29","author":"Zhang","year":"2026","journal-title":"Mater. Today Adv."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib106","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TMI.2022.3210714","article-title":"Segmentation and tracking of mammary epithelial organoids in Brightfield microscopy","volume":"42","author":"Hradecka","year":"2023","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib107","doi-asserted-by":"crossref","first-page":"4864","DOI":"10.1038\/s41467-022-32465-z","article-title":"Multiscale light-sheet organoid imaging framework","volume":"13","author":"de Medeiros","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib108","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0240802","article-title":"OrganoidTracker: efficient cell tracking using machine learning and manual error correction","volume":"15","author":"Kok","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib109","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.cmet.2025.01.002","article-title":"Lactate controls cancer stemness and plasticity through epigenetic regulation","volume":"37","author":"Nguyen","year":"2025","journal-title":"Cell Metab."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib110","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.51085","article-title":"CytoCensus, mapping cell identity and division in tissues and organs using machine learning","volume":"9","author":"Hailstone","year":"2020","journal-title":"eLife"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib111","doi-asserted-by":"crossref","first-page":"2400","DOI":"10.1038\/s41592-025-02845-6","article-title":"Cell tracking with accurate error prediction","volume":"22","author":"Betjes","year":"2025","journal-title":"Nat. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib112","article-title":"Analysis of organoid and immune cell co-cultures by machine learning-empowered image cytometry","volume":"10","author":"St\u00fcve","year":"2023","journal-title":"Front Med (Lausanne)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib113","doi-asserted-by":"crossref","DOI":"10.1111\/micc.12520","article-title":"Methods to label, image, and analyze the complex structural architectures of microvascular networks","volume":"26","author":"Corliss","year":"2019","journal-title":"Microcirculation"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib114","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s13206-023-00114-2","article-title":"Machine learning-aided three-dimensional morphological quantification of angiogenic vasculature in the multiculture microfluidic platform","volume":"17","author":"Lee","year":"2023","journal-title":"Biochip J."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib115","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1007\/s10439-023-03177-2","article-title":"Evaluation of the morphological and biological functions of vascularized microphysiological systems with supervised machine learning","volume":"51","author":"Tronolone","year":"2023","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib116","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2025.108953","article-title":"Label-free prediction of vascular connectivity in perfused microvascular networks in vitro","volume":"271","author":"Xu","year":"2025","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib117","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pbio.3003597","article-title":"A deep learning-based computational pipeline predicts developmental outcome in retinal organoids","volume":"24","author":"Afting","year":"2026","journal-title":"PLoS Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib118","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1039\/D2LC00596D","article-title":"D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images","volume":"22","author":"Abdul","year":"2022","journal-title":"Lab Chip"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib130","article-title":"Deep learning-powered scalable cancer organ chip for cancer precision medicine","volume":"13","author":"Yuan","year":"2026","journal-title":"Adv. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib119","doi-asserted-by":"crossref","DOI":"10.1126\/sciimmunol.abl6543","article-title":"BMP signaling in the intestinal epithelium drives a critical feedback loop to restrain IL-13-driven tuft cell hyperplasia","volume":"7","author":"Lindholm","year":"2022","journal-title":"Sci. Immunol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib120","doi-asserted-by":"crossref","DOI":"10.3390\/app152111311","article-title":"Multi-class segmentation and classification of intestinal organoids: YOLO stand-alone vs. hybrid machine learning pipelines","volume":"15","author":"Conte","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib121","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1039\/D0LC01010C","article-title":"Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning","volume":"20","author":"Abdul","year":"2020","journal-title":"Lab Chip"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib122","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2025.100446","article-title":"CLORG: a contrastive learning-based framework for morphological representation and classification of organoids","volume":"27","author":"Wei","year":"2025","journal-title":"Array"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib123","first-page":"13","article-title":"AI-enabled label free monitoring of TGF\u03b21-induced remodeling in iPSC-derived alveolar organoids","volume":"38","author":"Kim","year":"2026","journal-title":"Mater. Today Bio"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib124","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.cels.2023.08.002","article-title":"A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids","volume":"14","author":"Sockell","year":"2023","journal-title":"Cell Syst."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib125","article-title":"Molecular distinctions of bronchoalveolar and alveolar organoids under differentiation conditions","volume":"12","author":"Yu","year":"2024","journal-title":"Phys. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib126","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s13770-023-00563-8","article-title":"Deep learning model for predicting airway organoid differentiation","volume":"20","author":"Lim","year":"2023","journal-title":"Tissue Eng. Regen. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib127","doi-asserted-by":"crossref","first-page":"75","DOI":"10.23876\/j.krcp.22.017","article-title":"Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells","volume":"42","author":"Park","year":"2023","journal-title":"Kidney Res. Clin. Pract."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib128","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ymeth.2025.01.012","article-title":"Ins-ATP: deep estimation of ATP for organoid based on high throughput microscope images","volume":"235","author":"Bian","year":"2025","journal-title":"Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib129","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41598-024-73725-w","article-title":"A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids","volume":"14","author":"Takagi","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib131","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-49758-y","article-title":"Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid development","volume":"13","author":"Fujimura","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib132","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108322","article-title":"Image-based profiling and deep learning reveal morphological heterogeneity of colorectal cancer organoids","volume":"173","author":"Huang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib133","article-title":"Artificial intelligence-assisted high-throughput screening of printing conditions of Hydrogel architectures for accelerated diabetic wound healing","volume":"32","author":"Chen","year":"2022","journal-title":"Adv. Funct. Mater."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib134","doi-asserted-by":"crossref","first-page":"120","DOI":"10.15283\/ijsc24044","article-title":"Guidelines for manufacturing and application of organoids: liver","volume":"17","author":"Moon","year":"2024","journal-title":"Int. J. Stem Cells"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib135","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1364\/BOE.486666","article-title":"Quantifying the drug response of patient-derived organoid clusters by aggregated morphological indicators with multi-parameters based on optical coherence tomography","volume":"14","author":"Zhang","year":"2023","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib136","doi-asserted-by":"crossref","first-page":"8897","DOI":"10.1038\/s41467-025-63955-5","article-title":"HCS-3DX, a next-generation AI-driven automated 3D-oid high-content screening system","volume":"16","author":"Diosdi","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib137","doi-asserted-by":"crossref","first-page":"6714","DOI":"10.1021\/acssensors.5c01433","article-title":"Versatile image-assisted cell sorting by selective trapping with spatiotemporal multiparameter targeting","volume":"10","author":"Paul","year":"2025","journal-title":"ACS Sens."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib138","first-page":"12","article-title":"DOSS: microfluidic engineered system enables automated intelligent sorting and chemo-evaluation of droplet-based colorectal adenocarcinoma organoids","volume":"522","author":"Zhao","year":"2025","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib139","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-48343-7","article-title":"An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids","volume":"13","author":"Deininger","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib140","doi-asserted-by":"crossref","DOI":"10.3390\/s26010112","article-title":"Lightweight deep learning model for classification of normal and abnormal vasculature in organoid images","volume":"26","author":"Yun","year":"2025","journal-title":"Sensors (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib141","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s42003-024-07109-1","article-title":"A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids","volume":"7","author":"Asano","year":"2024","journal-title":"Commun. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib142","article-title":"Prediction of the hypothalamus-pituitary organoid formation using machine learning","volume":"5","author":"Matsumoto","year":"2025","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib143","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3389\/fncel.2020.00171","article-title":"Convolutional neural networks can predict retinal differentiation in retinal organoids","volume":"14","author":"Kegeles","year":"2020","journal-title":"Front. Cell. Neurosci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib144","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fbioe.2021.614324","article-title":"One-time optimization of advanced T cell culture media using a machine learning pipeline","volume":"9","author":"Grzesik","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib145","first-page":"8","article-title":"Connectomics of bone to brain-probing physical renderings of cellular experience","volume":"12","author":"Tate","year":"2021","journal-title":"Front. Physiol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib146","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1134\/S0006297915060061","article-title":"Bioluminescence assay for cell viability","volume":"80","author":"Lomakina","year":"2015","journal-title":"Biochemistry. Biokhimiia"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib147","first-page":"519","article-title":"A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids","volume":"20","author":"Yang","year":"2023","journal-title":"Cancer Biol. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib148","article-title":"OrganoidViT: a vision transformer-based deep learning model for segmenting viable and nonviable organoids in bright-field images","volume":"13","author":"Liu","year":"2026","journal-title":"BioDes. Manuf."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib149","doi-asserted-by":"crossref","DOI":"10.1063\/5.0189222","article-title":"Quantitative viability analysis of 3D tissue cultures using machine learning image segmentation","volume":"8","author":"Trettner","year":"2024","journal-title":"APL Bioeng."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib150","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1142\/S1758825121500721","article-title":"Implementing machine learning algorithms on finite element analyses data sets for selecting proper cellular structure","volume":"13","author":"Darvishi","year":"2021","journal-title":"Int. J. Appl. Mech."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib151","doi-asserted-by":"crossref","DOI":"10.1002\/admt.202400473","article-title":"Non-invasive quality control of organoid cultures using mesofluidic CSTR bioreactors and high-content imaging","volume":"10","author":"Charles","year":"2025","journal-title":"Adv. Mater. Technol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib152","article-title":"mTIPs: multimodal time-series imaging and prediction system for early differentiation of retinal organoids","author":"Wang","year":"2026","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib153","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1097\/CM9.0000000000003575","article-title":"Development and validation of deep learning for predicting the growth of ovarian cancer organoids","volume":"139","author":"Wu","year":"2026","journal-title":"Chin Med J (Engl)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib154","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1111\/cas.15396","article-title":"Integration of human inspection and artificial intelligence-based morphological typing of patient-derived organoids reveals interpatient heterogeneity of colorectal cancer","volume":"113","author":"Okamoto","year":"2022","journal-title":"Cancer Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib155","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1088\/1758-5090\/ad38df","article-title":"Advancing preclinical drug evaluation through automated 3D imaging for high-throughput screening with kidney organoids","volume":"16","author":"Oishi","year":"2024","journal-title":"Biofabrication"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib156","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1631\/bdm.2500119","article-title":"Precision organoid segmentation technique (POST): accurate organoid segmentation in challenging bright-field images","volume":"9","author":"Du","year":"2026","journal-title":"BioDes. Manuf."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib157","article-title":"Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models","volume":"2","author":"Metzger","year":"2022","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib158","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1038\/s42003-025-08205-6","article-title":"Development of an AI model for DILI-level prediction using liver organoid brightfield images","volume":"8","author":"Tan","year":"2025","journal-title":"Commun. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib159","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1631\/jzus.B2100701","article-title":"High-throughput \"read-on-ski\" automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids","volume":"23","author":"Wang","year":"2022","journal-title":"J. Zhejiang Univ. - Sci. B"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib160","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.parkreldis.2020.05.011","article-title":"Machine learning-assisted neurotoxicity prediction in human midbrain organoids","volume":"75","author":"Monzel","year":"2020","journal-title":"Parkinsonism Relat. Disorders"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib161","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isci.2025.113831","article-title":"Variability vs. phenotype: multimodal analysis of Dravet syndrome brain organoids powered by deep learning","volume":"28","author":"Turpin-Moreno","year":"2025","journal-title":"iScience"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib162","article-title":"Single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer","volume":"7","author":"Le Compte","year":"2023","journal-title":"npj Precis. Oncol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib163","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1038\/s41467-023-38832-8","article-title":"Drug screening at single-organoid resolution via bioprinting and interferometry","volume":"14","author":"Tebon","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib164","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1038\/nrd4366","article-title":"Phenotypic screening in cancer drug discovery - past, present and future","volume":"13","author":"Moffat","year":"2014","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib165","doi-asserted-by":"crossref","DOI":"10.1002\/mco2.70768","article-title":"Organoids: from bench to bedside applications","volume":"7","author":"Li","year":"2026","journal-title":"MedComm"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib166","article-title":"Iteration of tumor organoids in drug development: simplification and integration","volume":"18","author":"Zhao","year":"2025","journal-title":"Pharmaceuticals (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib167","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1038\/s41586-019-1289-x","article-title":"Individual brain organoids reproducibly form cell diversity of the human cerebral cortex","volume":"570","author":"Velasco","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib168","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1016\/j.stem.2023.09.003","article-title":"Tissue morphology influences the temporal program of human brain organoid development","volume":"30","author":"Chiaradia","year":"2023","journal-title":"Cell Stem Cell"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib169","doi-asserted-by":"crossref","DOI":"10.1016\/j.crtox.2024.100197","article-title":"iPSC-derived and Patient-derived organoids: applications and challenges in scalability and reproducibility as pre-clinical models","volume":"7","author":"Heinzelmann","year":"2024","journal-title":"Curr. Res. Toxicol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib170","doi-asserted-by":"crossref","first-page":"14375","DOI":"10.1002\/cam4.5943","article-title":"Standardization of organoid culture in cancer research","volume":"12","author":"Zhou","year":"2023","journal-title":"Cancer Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib171","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1007\/s00330-021-08251-8","article-title":"Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility","volume":"32","author":"Schurink","year":"2022","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib172","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/s41378-025-01141-9","article-title":"Strategies to control cellular spatial organization in microphysiological systems","volume":"12","author":"Truong","year":"2026","journal-title":"Microsyst. Nanoeng."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib173","first-page":"144","article-title":"Dynamic hydrogel mechanics in organoid engineering: from matrix design to translational paradigms","volume":"55","author":"Zhang","year":"2026","journal-title":"Bioact. Mater."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib174","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/s41580-023-00583-1","article-title":"Cell-extracellular matrix mechanotransduction in 3D","volume":"24","author":"Saraswathibhatla","year":"2023","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib175","doi-asserted-by":"crossref","DOI":"10.1063\/5.0198848","article-title":"Spatially defined microenvironment for engineering organoids","volume":"5","author":"Zhang","year":"2024","journal-title":"Biophysics reviews"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib176","doi-asserted-by":"crossref","DOI":"10.3390\/jcm15051937","article-title":"Clinical trial design and regulatory requirements for artificial intelligence as a medical device: a PRISMA-ScR-Guided scoping review of global guidance and evidence (2017-2025)","volume":"15","author":"Shanmugam","year":"2026","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib177","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3390\/biomimetics10120796","article-title":"Integrating new approach methodologies (NAMs) into preclinical regulatory evaluation of oncology drugs","volume":"10","author":"Mirlohi","year":"2025","journal-title":"Biomimetics"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib178","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.medj.2021.08.005","article-title":"Promises and challenges of organoid-guided precision medicine","volume":"2","author":"Bose","year":"2021","journal-title":"Med"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib179","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1472-6947-12-66","article-title":"Estimating the re-identification risk of clinical data sets","volume":"12","author":"Dankar","year":"2012","journal-title":"BMC Med. Inf. Decis. Making"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib180","doi-asserted-by":"crossref","DOI":"10.3390\/biology15131092","article-title":"Ethical challenges and governance strategies for microphysiological systems technology","volume":"15","author":"Zhao","year":"2026","journal-title":"Biology"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib181","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1186\/s13287-022-02950-9","article-title":"Organoids: a systematic review of ethical issues","volume":"13","author":"de Jongh","year":"2022","journal-title":"Stem Cell Res. Ther."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib182","doi-asserted-by":"crossref","DOI":"10.1016\/j.puhe.2025.105726","article-title":"AI and omics technologies in biobanking: applications and challenges for public health","volume":"243","author":"Venturini","year":"2025","journal-title":"Public Health"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib183","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/s41597-023-02367-w","article-title":"FAIR high content screening in bioimaging","volume":"10","author":"Hosseini","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib184","first-page":"525","article-title":"AI-enabled organoids: construction, analysis, and application","volume":"31","author":"Bai","year":"2024","journal-title":"Bioact. Mater."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib185","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1038\/s41597-022-01562-5","article-title":"Standard metadata for 3D microscopy","volume":"9","author":"Ropelewski","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib186","doi-asserted-by":"crossref","DOI":"10.1242\/dmm.052441","article-title":"Building a framework for reproducibility: the case for standardized data reporting and metadata integration in zebrafish research","volume":"18","author":"Ngu","year":"2025","journal-title":"Dis. Model. Mech."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib187","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13321-025-01021-4","article-title":"Addressing standardization and semantics in an electronic lab notebook for multidisciplinary use: labimotion","volume":"17","author":"Lin","year":"2025","journal-title":"J. Cheminf."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib188","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1038\/s41597-024-03449-z","article-title":"Biomedical data repository concepts and management principles","volume":"11","author":"Lin","year":"2024","journal-title":"Sci. Data"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib189","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/s41597-023-01968-9","article-title":"Developing a standardized but extendable framework to increase the findability of infectious disease datasets","volume":"10","author":"Tsueng","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib190","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107754","article-title":"Shaping pre-trained language models for task-specific embedding generation via consistency calibration","volume":"191","author":"Gao","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib191","doi-asserted-by":"crossref","DOI":"10.1016\/j.simpat.2023.102754","article-title":"Artificial intelligence foundation and pre-trained models: fundamentals, applications, opportunities, and social impacts","volume":"126","author":"Kolides","year":"2023","journal-title":"Simulat. Model. Pract. Theor."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib192","doi-asserted-by":"crossref","DOI":"10.1016\/j.crmeth.2026.101482","article-title":"OrgLine: a versatile pipeline for organoid morphometry using detector-guided prompts","author":"Deng","year":"2026","journal-title":"Cell Rep. Methods"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib193","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110031","article-title":"Benchmarking pathology foundation models: adaptation strategies and scenarios","volume":"190","author":"Lee","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib194","article-title":"From modality-specific to compositional foundation models for cell biology","volume":"17","author":"Bahrami","year":"2026","journal-title":"Cell Syst."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib195","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.csbj.2024.08.005","article-title":"Unveiling the black box: a systematic review of Explainable Artificial Intelligence in medical image analysis","volume":"24","author":"Muhammad","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib196","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"van der Velden","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib197","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1371\/journal.pcbi.1008462","article-title":"Biologically-informed neural networks guide mechanistic modeling from sparse experimental data","volume":"16","author":"Lagergren","year":"2020","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib198","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3390\/math13101664","article-title":"Using physics-informed neural networks for modeling biological and epidemiological dynamical systems","volume":"13","author":"Farea","year":"2025","journal-title":"Mathematics"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib199","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2025.1595291","article-title":"Visible neural networks for multi-omics integration: a critical review","volume":"8","author":"Selby","year":"2025","journal-title":"Front. Artif. Intell."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib200","article-title":"Mechanochemical regulation of organoid morphogenesis: integrated signaling circuits in engineered microenvironments","volume":"37","author":"Zhang","year":"2026","journal-title":"Mater. Today Bio"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib201","doi-asserted-by":"crossref","DOI":"10.1016\/j.bioadv.2025.214323","article-title":"Artificial Intelligence tool for prediction of ECM mimics hydrogel formulations via click chemistry","volume":"175","author":"Cadamuro","year":"2025","journal-title":"Biomater. Adv."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib202","doi-asserted-by":"crossref","first-page":"4781","DOI":"10.1073\/pnas.1912656117","article-title":"Spatial mapping of tissue properties in vivo reveals a 3D stiffness gradient in the mouse limb bud","volume":"117","author":"Zhu","year":"2020","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib203","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1038\/s41578-019-0129-9","article-title":"Engineered materials for organoid systems","volume":"4","author":"Kratochvil","year":"2019","journal-title":"Nat. Rev. Mater."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib204","article-title":"Engineered matrices enable the culture of human patient-derived intestinal organoids","volume":"8","author":"Hunt","year":"2021","journal-title":"Advanced science (Weinheim, Baden-Wurttemberg, Germany)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib205","doi-asserted-by":"crossref","DOI":"10.3390\/ijms262110753","article-title":"Organ-on-a-Chip: a roadmap for translational research in human and veterinary medicine","volume":"26","author":"Surina","year":"2025","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib206","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/s43586-022-00118-6","article-title":"A guide to the organ-on-a-chip","volume":"2","author":"Leung","year":"2022","journal-title":"Nat. Rev. Methods Primers"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib207","doi-asserted-by":"crossref","DOI":"10.1016\/j.yexcr.2022.113218","article-title":"Leveraging the model-experiment loop: examples from cellular slime mold chemotaxis","volume":"418","author":"Zhu","year":"2022","journal-title":"Exp. Cell Res."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib208","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1038\/s43588-025-00870-1","article-title":"In silico biological discovery with large perturbation models","volume":"5","author":"Miladinovic","year":"2025","journal-title":"Nat. Comput. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib209","series-title":"Drugs and a Methodological Compendium : from Bench to Bedside","first-page":"149","author":"Negi","year":"2023"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib210","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2025.104854","article-title":"Monitoring strategies for continuous evaluation of deployed clinical prediction models","volume":"168","author":"Kim","year":"2025","journal-title":"J. Biomed. Inf."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib211","article-title":"Beyond model development in healthcare AI: post-development robustness, post-deployment monitoring, and lifecycle Governance-A scoping review of reviews","volume":"14","author":"El Arab","year":"2026","journal-title":"Healthcare (Basel, Switzerland)"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib212","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1038\/s41568-026-00922-2","article-title":"Advancing AI for multi-omics and clinical data integration in basic and translational cancer research","volume":"26","author":"Liu","year":"2026","journal-title":"Nat. Rev. Cancer"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib213","doi-asserted-by":"crossref","DOI":"10.1016\/j.jik.2024.100598","article-title":"Data governance & quality management\u2014Innovation and breakthroughs across different fields","volume":"9","author":"Bernardo","year":"2024","journal-title":"J. Innov. Knowl."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib214","first-page":"45","article-title":"Artificial intelligence virtual organoids (AIVOs)","volume":"59","author":"Bai","year":"2026","journal-title":"Bioact. Mater."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib215","doi-asserted-by":"crossref","DOI":"10.1002\/btm2.10641","article-title":"AI-organoid integrated systems for biomedical studies and applications","volume":"9","author":"Maramraju","year":"2024","journal-title":"Bioeng. Transl. Med."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib216","doi-asserted-by":"crossref","DOI":"10.1098\/rstb.2015.0520","article-title":"Vertex models: from cell mechanics to tissue morphogenesis","volume":"372","author":"Alt","year":"2017","journal-title":"Phil. Trans. Roy. Soc. Lond. B Biol. Sci."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib217","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007459","article-title":"From energy to cellular forces in the Cellular potts model: an algorithmic approach","volume":"15","author":"Rens","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bioactmat.2026.08.043_bib218","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2024.111600","article-title":"PDE-based control synthesis for a planar cable-driven continuum arm","volume":"163","author":"Han","year":"2024","journal-title":"Automatica"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib219","doi-asserted-by":"crossref","DOI":"10.3390\/cells15020177","article-title":"Cell-Based computational models of organoids: a systematic review","volume":"15","author":"Neagu","year":"2026","journal-title":"Cells"},{"key":"10.1016\/j.bioactmat.2026.08.043_bib220","doi-asserted-by":"crossref","unstructured":"Pushpa Ramesan, S. et al. Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling. Trends Biotechnol. https:\/\/doi.org\/10.1016\/j.tibtech.2026.01.009.","DOI":"10.1016\/j.tibtech.2026.01.009"}],"container-title":["Bioactive Materials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452199X26005062?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452199X26005062?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T18:05:47Z","timestamp":1789668347000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2452199X26005062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,2]]},"references-count":220,"alternative-id":["S2452199X26005062"],"URL":"https:\/\/doi.org\/10.1016\/j.bioactmat.2026.08.043","relation":{},"ISSN":["2452-199X"],"issn-type":[{"value":"2452-199X","type":"print"}],"subject":[],"published":{"date-parts":[[2027,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight","name":"articletitle","label":"Article Title"},{"value":"Bioactive Materials","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bioactmat.2026.08.043","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.","name":"copyright","label":"Copyright"}]}}