Artificial Intelligence for Organoids Multidimensional Assessment

Abstract

Organoids are tissue analogues formed through in vitro three-dimensional culture of stem cells, possessing specific spatial structures. Organoids have become integral to various biomedical fields, including disease pathogenesis, model construction, regenerative and precision medicine, drug screening, tissue and organ development, toxicology, and pathological analysis. However, the diversity of organoid types and variations in their production processes have led to inconsistencies in their application for assessment and analysis. To date, no comprehensive standards or guidelines for evaluating organoids have been established. Artificial intelligence (AI) technology is extensively employed in biomedical image analysis, data processing, and molecular structure prediction, demonstrating benefits in the assessment of organoids. This review will examine the application of AI across various aspects of organoid assessment, such as omics, histology, morphology, functional properties, and drug screening, with the goal of offering new perspectives on organoid assessment.

Keywords

artificial intelligence / assessment / drug screening / organoids / single-cell sequencing

References

[1] H. Clevers, “Modeling Development and Disease With Organoids,” Cell 165, no. 7 (2016): 1586–1597.

[2] S. P. Pașca, P. Arlotta, H. S. Bateup, et al., “A Nomenclature Consensus for Nervous System Organoids and Assembloids,” Nature 609, no. 7929 (2022): 907–910.

[3] N. Gjorevski, M. Nikolaev, T. E. Brown, et al., “Tissue Geometry Drives Deterministic Organoid Patterning,” Science 375, no. 6576 (2022): eaaw9021.

[4] O. Chaudhuri, J. Cooper-White, P. A. Janmey, D. J. Mooney, and V. B. Shenoy, “Effects of Extracellular Matrix Viscoelasticity on Cellular Behaviour,” Nature 584, no. 7822 (2020): 535–546.

[5] J. S. Fleck, S. M. J. Jansen, D. Wollny, et al., “Inferring and Perturbing Cell Fate Regulomes in Human Brain Organoids,” Nature 621, no. 7978 (2023): 365–372.

[6] N. Prior, P. Inacio, and M. Huch, “Liver Organoids: From Basic Research to Therapeutic Applications,” Gut 68, no. 12 (2019): 2228–2237.

[7] Y. Pan, H. Han, H. Hu, et al., “KMT2D Deficiency Drives Lung Squamous Cell Carcinoma and Hypersensitivity to RTK-RAS Inhibition,” Cancer Cell 41, no. 1 (2023): 88–105.e8.

[8] M. Takasato, P. X. Er, H. S. Chiu, et al., “Kidney Organoids From Human iPS Cells Contain Multiple Lineages and Model Human Nephrogenesis,” Nature 526, no. 7574 (2015): 564–568.

[9] L. Drakhlis, S. Biswanath, C. M. Farr, et al., “Human Heart-Forming Organoids Recapitulate Early Heart and Foregut Development,” Nature Biotechnology 39, no. 6 (2021): 737–746.

[10] C. Bouffi, K. A. Wikenheiser-Brokamp, P. Chaturvedi, et al., “In Vivo Development of Immune Tissue in Human Intestinal Organoids Transplanted into Humanized Mice,” Nature Biotechnology 41, no. 6 (2023): 824–831.

[11] Y. Hu, H. Zhang, S. Wang, et al., “Bone/Cartilage Organoid On-Chip: Construction Strategy and Application,” Bioactive Materials 25, no. 7 (2023): 29–41.

[12] J. Wang, Y. Wu, G. Li, et al., “Engineering Large-Scale Self-Mineralizing Bone Organoids With Bone Matrix-Inspired Hydroxyapatite Hybrid Bioinks,” Advanced Materials 36, no. 30 (2024): e2309875.

[13] S. E. Park, A. Georgescu, and D. Huh, “Organoids-on-a-Chip,” Science 364, no. 6444 (2019): 960–965.

[14] W. Hu and M. A. Lazar, “Modelling Metabolic Diseases and Drug Response Using Stem Cells and Organoids,” Nature Reviews Endocrinology 18, no. 12 (2022): 744–759.

[15] E. J. Villablanca, K. Selin, and C. R. H. Hedin, “Mechanisms of Mucosal Healing: Treating Inflammatory Bowel Disease Without Immunosuppression?,” Nature Reviews Gastroenterology & Hepatology 19, no. 8 (2022): 493–507.

[16] O. Kopper, C. J. de Witte, K. Lõhmussaar, et al., “An Organoid Platform for Ovarian Cancer Captures Intra- and Interpatient Heterogeneity,” Nature Medicine 25, no. 5 (2019): 838–849.

[17] S. Chen, X. Chen, Z. Geng, and J. Su, “The Horizon of Bone Organoid: A Perspective on Construction and Application,” Bioactive Materials 18 (2022): 15–25.

[18] H. Liu and J. Su, “Organoid Extracellular Vesicle-Based Therapeutic Strategies for Bone Therapy,” Biomaterials Translational 4, no. 4 (2023): 199–212.

[19] C. Shen, J. Wang, G. Li, et al., “Boosting Cartilage Repair With Silk Fibroin-DNA Hydrogel-Based Cartilage Organoid Precursor,” Bioactive Materials 35 (2024): 429–444.

[20] X. Li, S. Sheng, G. Li, et al., “Research Progress in Hydrogels for Cartilage Organoids,” Advanced Healthcare Materials 13, no. 22 (2024): e2400431.

[21] M. Eisenstein, “Organoids Open Fresh Paths to Biomedical Advances,” Nature 612, no. 7940 (2022): S34–S35.

[22] S. Rauth, S. Karmakar, S. K. Batra, and M. P. Ponnusamy, “Recent Advances in Organoid Development and Applications in Disease Modeling,” Biochimica et Biophysica Acta (BBA)—Reviews on Cancer 1875, no. 2 (2021): 188527.

[23] S. Deng, F. Zhu, K. Dai, J. Wang, and C. Liu, “Harvest of Functional Mesenchymal Stem Cells Derived From In Vivo Osteo-Organoids,” Biomaterials Translational 4, no. 4 (2023): 270–279.

[24] J. Lv, X. Du, M. Wang, J. Su, Y. Wei, and C. Xu, “Construction of Tumor Organoids and Their Application to Cancer Research and Therapy,” Theranostics 14, no. 3 (2024): 1101–1125.

[25] Y. Gu, W. Zhang, X. Wu, Y. Zhang, K. Xu, and J. Su, “Organoid Assessment Technologies,” Clinical and Translational Medicine 13, no. 12 (2023): e1499.

[26] C. Bock, M. Boutros, J. G. Camp, et al., “The Organoid Cell Atlas,” Nature Biotechnology 39, no. 1 (2021): 13–17.

[27] Y. Gu, Y. Hu, H. Zhang, S. Wang, K. Xu, and J. Su, “Single-Cell RNA Sequencing in Osteoarthritis,” Cell Proliferation 56, no. 12 (2023): e13517.

[28] M. Hofer and M. P. Lutolf, “Engineering Organoids,” Nature Reviews Materials 6, no. 5 (2021): 402–420.

[29] H. Gehart, J. H. van Es, K. Hamer, et al., “Identification of Enteroendocrine Regulators by Real-Time Single-Cell Differentiation Mapping,” Cell 176, no. 5 (2019): 1158–1173.e16.

[30] M. Bannier-Hélaouët, Y. Post, J. Korving, et al., “Exploring the Human Lacrimal Gland Using Organoids and Single-Cell Sequencing,” Cell Stem Cell 28, no. 7 (2021): 1221–1232.e7.

[31] J. Liang, J. Wei, J. Cao, et al., “In-Organoid Single-Cell CRISPR Screening Reveals Determinants of Hepatocyte Differentiation and Maturation,” Genome Biology 24, no. 1 (2023): 251.

[32] L. Bai, Y. Wu, G. Li, W. Zhang, H. Zhang, and J. Su, “AI-Enabled Organoids: Construction, Analysis, and Application,” Bioactive Materials 31 (2024): 525–548.

[33] L. Bai, Z. Xia, J. T. Triffitt, and J. Su, “Generation Artificial Intelligence (GenAI) and Biomaterials Translational: Steering Innovation Without Misdirection,” Biomaterials Translational 5, no. 1 (2024): 1–2.

[34] X. Bian, G. Li, C. Wang, et al., “A Deep Learning Model for Detection and Tracking in High-Throughput Images of Organoid,” Computers in Biology and Medicine 134 (2021): 104490.

[35] A. V. Singh, V. Chandrasekar, N. Paudel, et al., “Integrative Toxicogenomics: Advancing Precision Medicine and Toxicology Through Artificial Intelligence and OMICs Technology,” Biomedicine & Pharmacotherapy 163 (2023): 114784.

[36] H. Azizgolshani, J. R. Coppeta, E. M. Vedula, et al., “High-Throughput Organ-on-Chip Platform With Integrated Programmable Fluid Flow and Real-Time Sensing for Complex Tissue Models in Drug Development Workflows,” Lab on a Chip 21, no. 8 (2021): 1454–1474.

[37] L. Puca, R. Bareja, D. Prandi, et al., “Patient Derived Organoids to Model Rare Prostate Cancer Phenotypes,” Nature Communications 9, no. 1 (2018): 2404.

[38] X. Zheng, M. A. Betjes, P. Ender, et al., “Organoid Cell Fate Dynamics in Space and Time,” Science Advances 9, no. 33 (2023): eadd6480.

[39] K. K. Mak and M. R. Pichika, “Artificial Intelligence in Drug Development: Present Status and Future Prospects,” Drug Discovery Today 24, no. 3 (2019): 773–780.

[40] R. M. Karvas, S. A. Khan, S. Verma, et al., “Stem-Cell-Derived Trophoblast Organoids Model Human Placental Development and Susceptibility to Emerging Pathogens,” Cell Stem Cell 29, no. 5 (2022): 810–825.e8.

[41] J. Criscione, Z. Rezaei, C. M. Hernandez Cantu, S. Murphy, S. R. Shin, and D. H. Kim, “Heart-on-a-Chip Platforms and Biosensor Integration for Disease Modeling and Phenotypic Drug Screening,” Biosensors and Bioelectronics 220 (2023): 114840.

[42] I. Bentwich, “Pharma's Bio-AI Revolution,” Drug Discovery Today 28, no. 5 (2023): 103515.

[43] S. Rajasekar, D. S. Y. Lin, L. Abdul, et al., “IFlowPlate—A Customized 384-well Plate for the Culture of Perfusable Vascularized Colon Organoids,” Advanced Materials 32, no. 46 (2020): e2002974.

[44] D. D. Sahtoe, A. Coscia, N. Mustafaoglu, et al., “Transferrin Receptor Targeting by De Novo Sheet Extension,” Proceedings of the National Academy of Sciences 118, no. 17 (2021): e2021569118.

[45] Z. Lyu, J. Park, K. M. Kim, et al., “A Neurovascular-Unit-on-a-Chip for the Evaluation of the Restorative Potential of Stem Cell Therapies for Ischaemic Stroke,” Nature Biomedical Engineering 5, no. 8 (2021): 847–863.

[46] T. E. Park, N. Mustafaoglu, A. Herland, et al., “Hypoxia-Enhanced Blood-Brain Barrier Chip Recapitulates Human Barrier Function and Shuttling of Drugs and Antibodies,” Nature Communications 10, no. 1 (2019): 2621.

[47] L. Si, H. Bai, M. Rodas, et al., “A Human-Airway-on-a-Chip for the Rapid Identification of Candidate Antiviral Therapeutics and Prophylactics,” Nature Biomedical Engineering 5, no. 8 (2021): 815–829.

[48] Y. S. Zhang, A. Arneri, S. Bersini, et al., “Bioprinting 3D Microfibrous Scaffolds for Engineering Endothelialized Myocardium and Heart-on-a-Chip,” Biomaterials 110 (2016): 45–59.

[49] T. Tao, P. Deng, Y. Wang, et al., “Microengineered Multi-Organoid System From hiPSCs to Recapitulate Human Liver-Islet Axis in Normal and Type 2 Diabetes,” Advanced Science 9, no. 5 (2022): e2103495.

[50] K. M. Bircsak, R. DeBiasio, M. Miedel, et al., “A 3D Microfluidic Liver Model for High Throughput Compound Toxicity Screening in the Organoplate®,” Toxicology 450 (2021): 152667.

[51] T. Tran, C. J. Song, T. Nguyen, et al., “A Scalable Organoid Model of Human Autosomal Dominant Polycystic Kidney Disease for Disease Mechanism and Drug Discovery,” Cell Stem Cell 29, no. 7 (2022): 1083–1101.e7.

[52] K. A. Homan, N. Gupta, K. T. Kroll, et al., “Flow-Enhanced Vascularization and Maturation of Kidney Organoids In Vitro,” Nature Methods 16, no. 3 (2019): 255–262.

[53] I. Lukonin, D. Serra, L. Challet Meylan, et al., “Phenotypic Landscape of Intestinal Organoid Regeneration,” Nature 586, no. 7828 (2020): 275–280.

[54] M. R. Nelson, D. Ghoshal, J. C. Mejías, D. F. Rubio, E. Keith, and K. Roy, “A Multi-Niche Microvascularized Human Bone Marrow (hBM) on-a-Chip Elucidates Key Roles of the Endosteal Niche in hBM Physiology,” Biomaterials 270 (2021): 120683.

[55] K. Achberger, C. Probst, J. Haderspeck, et al., “Merging Organoid and Organ-on-a-Chip Technology to Generate Complex Multi-Layer Tissue Models in a Human Retina-on-a-Chip Platform,” eLife 8 (2019): e46188.

[56] B. A. Hassell, G. Goyal, E. Lee, et al., “Human Organ Chip Models Recapitulate Orthotopic Lung Cancer Growth, Therapeutic Responses, and Tumor Dormancy In Vitro,” Cell Reports 21, no. 2 (2017): 508–516.

[57] X. Cui, C. Ma, V. Vasudevaraja, et al., “Dissecting the Immunosuppressive Tumor Microenvironments in Glioblastoma-on-a-Chip for Optimized PD-1 Immunotherapy,” eLife 9 (2020): e52253.

[58] M. R. Carvalho, D. Barata, L. M. Teixeira, et al., “Colorectal Tumor-on-a-Chip System: A 3D Tool for Precision Onco-Nanomedicine,” Science Advances 5, no. 5 (2019): eaaw1317.

[59] H. Cai, Z. Ao, C. Tian, et al., “Engineering Human Spinal Microphysiological Systems to Model Opioid-Induced Tolerance,” Bioactive Materials 22 (2023): 482–490.

[60] C. Blundell, Y. S. Yi, L. Ma, et al., “Placental Drug Transport-on-a-Chip: A Microengineered In Vitro Model of Transporter-Mediated Drug Efflux in the Human Placental Barrier,” Advanced Healthcare Materials 7, no. 2 (2018): 1700786.

[61] C. M. França, A. Tahayeri, N. S. Rodrigues, et al., “The Tooth on-a-Chip: A Microphysiologic Model System Mimicking the Biologic Interface of the Tooth With Biomaterials,” Lab on a Chip 20, no. 2 (2020): 405–413.

[62] J. A. Bagley, D. Reumann, S. Bian, J. Lévi-Strauss, and J. A. Knoblich, “Fused Cerebral Organoids Model Interactions Between Brain Regions,” Nature Methods 14, no. 7 (2017): 743–751.

[63] X. Qian, H. N. Nguyen, M. M. Song, et al., “Brain-Region-Specific Organoids Using Mini-Bioreactors for Modeling ZIKV Exposure,” Cell 165, no. 5 (2016): 1238–1254.

[64] R. A. Wimmer, A. Leopoldi, M. Aichinger, et al., “Human Blood Vessel Organoids as a Model of Diabetic Vasculopathy,” Nature 565, no. 7740 (2019): 505–510.

[65] M. A. Lancaster, M. Renner, C. A. Martin, et al., “Cerebral Organoids Model Human Brain Development and Microcephaly,” Nature 501, no. 7467 (2013): 373–379.

[66] P. Hoang, J. Wang, B. R. Conklin, K. E. Healy, and Z. Ma, “Generation of Spatial-Patterned Early-Developing Cardiac Organoids Using Human Pluripotent Stem Cells,” Nature Protocols 13, no. 4 (2018): 723–737.

[67] A. Q. Lam, B. S. Freedman, R. Morizane, P. H. Lerou, M. T. Valerius, and J. V. Bonventre, “Rapid and Efficient Differentiation of Human Pluripotent Stem Cells Into Intermediate Mesoderm That Forms Tubules Expressing Kidney Proximal Tubular Markers,” Journal of the American Society of Nephrology 25, no. 6 (2014): 1211–1225.

[68] Y. W. Chen, S. X. Huang, A. L. R. T. de Carvalho, et al., “A Three-Dimensional Model of Human Lung Development and Disease From Pluripotent Stem Cells,” Nature Cell Biology 19, no. 5 (2017): 542–549.

[69] J. Candiello, T. S. P. Grandhi, S. K. Goh, et al., “3D Heterogeneous Islet Organoid Generation From Human Embryonic Stem Cells Using a Novel Engineered Hydrogel Platform,” Biomaterials 177 (2018): 27–39.

[70] N. Gjorevski, N. Sachs, A. Manfrin, et al., “Designer Matrices for Intestinal Stem Cell and Organoid Culture,” Nature 539, no. 7630 (2016): 560–564.

[71] T. A. Forbes, S. E. Howden, K. Lawlor, et al., “Patient-Ipsc-Derived Kidney Organoids Show Functional Validation of a Ciliopathic Renal Phenotype and Reveal Underlying Pathogenetic Mechanisms,” American Journal of Human Genetics 102, no. 5 (2018): 816–831.

[72] A. Lowe, R. Harris, P. Bhansali, A. Cvekl, and W. Liu, “Intercellular Adhesion-Dependent Cell Survival and ROCK-Regulated Actomyosin-Driven Forces Mediate Self-Formation of a Retinal Organoid,” Stem Cell Reports 6, no. 5 (2016): 743–756.

[73] T. Takebe, K. Sekine, M. Enomura, et al., “Vascularized and Functional Human Liver From an iPSC-Derived Organ Bud Transplant,” Nature 499, no. 7459 (2013): 481–484.

[74] M. Czerwinski and J. R. Spence, “Hacking the Matrix,” Cell Stem Cell 20, no. 1 (2017): 9–10.

[75] B. A. Lindborg, J. H. Brekke, A. L. Vegoe, et al., “Rapid Induction of Cerebral Organoids From Human Induced Pluripotent Stem Cells Using a Chemically Defined Hydrogel and Defined Cell Culture Medium,” Stem Cells Translational Medicine 5, no. 7 (2016): 970–979.

[76] B. R. Dye, P. H. Dedhia, A. J. Miller, et al., “A Bioengineered Niche Promotes In Vivo Engraftment and Maturation of Pluripotent Stem Cell Derived Human Lung Organoids,” eLife 5 (2016): e19732.

[77] R. Cruz-Acuña, M. Quirós, A. E. Farkas, et al., “Synthetic Hydrogels for Human Intestinal Organoid Generation and Colonic Wound Repair,” Nature Cell Biology 19, no. 11 (2017): 1326–1335.

[78] N. Broguiere, L. Isenmann, C. Hirt, et al., “Growth of Epithelial Organoids in a Defined Hydrogel,” Advanced Materials 30, no. 43 (2018): e1801621.

[79] C. Oleaga, A. Riu, S. Rothemund, et al., “Investigation of the Effect of Hepatic Metabolism on Off-Target Cardiotoxicity in a Multi-Organ Human-on-a-Chip System,” Biomaterials 182 (2018): 176–190.

[80] T. Sasserath, J. W. Rumsey, C. W. McAleer, et al., “Differential Monocyte Actuation in a Three-Organ Functional Innate Immune System-on-a-Chip,” Advanced Science 7, no. 13 (2020): 2000323.

[81] C. Oleaga, C. Bernabini, A. S. T. Smith, et al., “Multi-Organ Toxicity Demonstration in a Functional Human In Vitro System Composed of Four Organs,” Scientific Reports 6 (2016): 20030.

[82] L. Vernetti, A. Gough, N. Baetz, et al., “Functional Coupling of Human Microphysiology Systems: Intestine, Liver, Kidney Proximal Tubule, Blood-Brain Barrier and Skeletal Muscle,” Scientific Reports 7 (2017): 42296.

[83] C. W. McAleer, C. J. Long, D. Elbrecht, et al., “Multi-Organ System for the Evaluation of Efficacy and Off-Target Toxicity of Anticancer Therapeutics,” Science Translational Medicine 11, no. 497 (2019): eaav1386.

[84] S. A. P. Rajan, J. Aleman, M. Wan, et al., “Probing Prodrug Metabolism and Reciprocal Toxicity With an Integrated and Humanized Multi-Tissue Organ-on-a-Chip Platform,” Acta Biomaterialia 106 (2020): 124–135.

[85] A. Skardal, J. Aleman, S. Forsythe, et al., “Drug Compound Screening in Single and Integrated Multi-Organoid Body-on-a-Chip Systems,” Biofabrication 12, no. 2 (2020): 025017.

[86] R. Novak, M. Ingram, S. Marquez, et al., “Robotic Fluidic Coupling and Interrogation of Multiple Vascularized Organ Chips,” Nature Biomedical Engineering 4, no. 4 (2020): 407–420.

[87] M. Nikolaev, O. Mitrofanova, N. Broguiere, et al., “Homeostatic Mini-Intestines Through Scaffold-Guided Organoid Morphogenesis,” Nature 585, no. 7826 (2020): 574–578.

[88] D. Wang, J. Wang, L. Bai, et al., “Long-Term Expansion of Pancreatic Islet Organoids From Resident Procr(+) Progenitors,” Cell 180, no. 6 (2020): 1198–1211.e19.

[89] J. Wang, W. Song, R. Yang, et al., “Endothelial Wnts Control Mammary Epithelial Patterning via Fibroblast Signaling,” Cell Reports 34, no. 13 (2021): 108897.

[90] W. Weng, T. Meng, Q. Zhao, et al., “Antibody-Exatecan Conjugates With a Novel Self-Immolative Moiety Overcome Resistance in Colon and Lung Cancer,” Cancer Discovery 13, no. 4 (2023): 950–973.

[91] C. A. Rimland, S. G. Tilson, C. M. Morell, et al., “Regional Differences in Human Biliary Tissues and Corresponding In Vitro-Derived Organoids,” Hepatology 73, no. 1 (2021): 247–267.

[92] F. Sampaziotis, D. Muraro, O. C. Tysoe, et al., “Cholangiocyte Organoids Can Repair Bile Ducts After Transplantation in the Human Liver,” Science 371, no. 6531 (2021): 839–846.

[93] M. Huch, H. Gehart, R. van Boxtel, et al., “Long-Term Culture of Genome-Stable Bipotent Stem Cells From Adult Human Liver,” Cell 160, no. 1–2 (2015): 299–312.

[94] L. Broutier, A. Andersson-Rolf, C. J. Hindley, et al., “Culture and Establishment of Self-Renewing Human and Mouse Adult Liver and Pancreas 3D Organoids and Their Genetic Manipulation,” Nature Protocols 11, no. 9 (2016): 1724–1743.

[95] D. Bovard, A. Sandoz, K. Luettich, et al., “A Lung/Liver-on-a-Chip Platform for Acute and Chronic Toxicity Studies,” Lab on a Chip 18, no. 24 (2018): 3814–3829.

[96] C. P. Pires de Mello, C. Carmona-Moran, C. W. McAleer, et al., “Microphysiological Heart-Liver Body-on-a-Chip System With a Skin Mimic for Evaluating Topical Drug Delivery,” Lab on a Chip 20, no. 4 (2020): 749–759.

[97] J. Kühnl, T. P. Tao, K. Brandmair, et al., “Characterization of Application Scenario-Dependent Pharmacokinetics and Pharmacodynamic Properties of Permethrin and Hyperforin in a Dynamic Skin and Liver Multi-Organ-Chip Model,” Toxicology 448 (2021): 152637.

[98] N. Tsamandouras, W. L. K. Chen, C. D. Edington, C. L. Stokes, L. G. Griffith, and M. Cirit, “Integrated Gut and Liver Microphysiological Systems for Quantitative In Vitro Pharmacokinetic Studies,” AAPS Journal 19, no. 5 (2017): 1499–1512.

[99] Y. Baert, I. Ruetschle, W. Cools, et al., “A Multi-Organ-Chip Co-Culture of Liver and Testis Equivalents: A First Step Toward a Systemic Male Reprotoxicity Model,” Human Reproduction 35, no. 5 (2020): 1029–1044.

[100] J. Theobald, M. A. Abu El Maaty, N. Kusterer, et al., “In Vitro Metabolic Activation of Vitamin D3 by Using a Multi-Compartment Microfluidic Liver-Kidney Organ on Chip Platform,” Scientific Reports 9, no. 1 (2019): 4616.

[101] D. Liu, S. Jiao, J. Wei, et al., “Investigation of Absorption, Metabolism and Toxicity of Ginsenosides Compound K Based on Human Organ Chips,” International Journal of Pharmaceutics 587 (2020): 119669.

[102] A. Skardal, S. V. Murphy, M. Devarasetty, et al., “Multi-Tissue Interactions in an Integrated Three-Tissue Organ-on-a-Chip Platform,” Scientific Reports 7, no. 1 (2017): 8837.

[103] S. Xiao, J. R. Coppeta, H. B. Rogers, et al., “A Microfluidic Culture Model of the Human Reproductive Tract and 28-Day Menstrual Cycle,” Nature Communications 8 (2017): 14584.

[104] X. Wang, M. Cirit, J. S. Wishnok, L. G. Griffith, and S. R. Tannenbaum, “Analysis of an Integrated Human Multiorgan Microphysiological System for Combined Tolcapone Metabolism and Brain Metabolomics,” Analytical Chemistry 91, no. 13 (2019): 8667–8675.

[105] C. Zhou, Y. Wu, Z. Wang, et al., “Standardization of Organoid Culture in Cancer Research,” Cancer Medicine 12, no. 13 (2023): 14375–14386.

[106] Y. Zhang, G. Li, J. Wang, F. Zhou, X. Ren, and J. Su, “Small Joint Organoids 3D Bioprinting: Construction Strategy and Application,” Small 20, no. 8 (2024): e2302506.

[107] M. Abulaiti, Y. Yalikun, K. Murata, et al., “Establishment of a Heart-on-a-Chip Microdevice Based on Human iPS Cells for the Evaluation of Human Heart Tissue Function,” Scientific Reports 10, no. 1 (2020): 19201.

[108] W. Zhang, J. Li, J. Zhou, A. Rastogi, and S. Ma, “Translational Organoid Technology—The Convergence of Chemical, Mechanical, and Computational Biology,” Trends in Biotechnology 40, no. 9 (2022): 1121–1135.

[109] S. J. Mun, J. S. Ryu, M. O. Lee, et al., “Generation of Expandable Human Pluripotent Stem Cell-Derived Hepatocyte-Like Liver Organoids,” Journal of Hepatology 71, no. 5 (2019): 970–985.

[110] Y. Hirami, M. Mandai, S. Sugita, et al., “Safety and Stable Survival of Stem-Cell-Derived Retinal Organoid for 2 Years in Patients With Retinitis Pigmentosa,” Cell Stem Cell 30, no. 12 (2023): 1585–1596.e6.

[111] P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in Health and Medicine,” Nature Medicine 28, no. 1 (2022): 31–38.

[112] Z. Li, P. Song, G. Li, et al., “AI Energized Hydrogel Design, Optimization and Application in Biomedicine,” Materials Today Bio 25 (2024): 101014.

[113] J. Lipkova, R. J. Chen, B. Chen, et al., “Artificial Intelligence for Multimodal Data Integration in Oncology,” Cancer Cell 40, no. 10 (2022): 1095–1110.

[114] Q. Zeng, C. Klein, S. Caruso, et al., “Artificial Intelligence Predicts Immune and Inflammatory Gene Signatures Directly From Hepatocellular Carcinoma Histology,” Journal of Hepatology 77, no. 1 (2022): 116–127.

[115] L. Jin, F. Shi, Q. Chun, et al., “Artificial Intelligence Neuropathologist for Glioma Classification Using Deep Learning on Hematoxylin and Eosin Stained Slide Images and Molecular Markers,” Neuro-Oncology 23, no. 1 (2021): 44–52.

[116] M. Iacucci, T. L. Parigi, R. Del Amor, et al., “Artificial Intelligence Enabled Histological Prediction of Remission or Activity and Clinical Outcomes in Ulcerative Colitis,” Gastroenterology 164, no. 7 (2023): 1180–1188.e2.

[117] Y. Zhu, R. Huang, Z. Wu, S. Song, L. Cheng, and R. Zhu, “Deep Learning-Based Predictive Identification of Neural Stem Cell Differentiation,” Nature Communications 12, no. 1 (2021): 2614.

[118] B. Huang, H. Huang, S. Zhang, et al., “Artificial Intelligence in Pancreatic Cancer,” Theranostics 12, no. 16 (2022): 6931–6954.

[119] Q. Lin, T. Li, C. Cao, Y. Cao, Z. Man, and H. Wang, “Deep Learning Based Automated Diagnosis of Bone Metastases With SPECT Thoracic Bone Images,” Scientific Reports 11, no. 1 (2021): 4223.

[120] F. W. Pun, I. V. Ozerov, and A. Zhavoronkov, “AI-Powered Therapeutic Target Discovery,” Trends in Pharmacological Sciences 44, no. 9 (2023): 561–572.

[121] D. Paul, G. Sanap, S. Shenoy, D. Kalyane, K. Kalia, and R. K. Tekade, “Artificial Intelligence in Drug Discovery and Development,” Drug Discovery Today 26, no. 1 (2021): 80–93.

[122] J. Vamathevan, D. Clark, P. Czodrowski, et al., “Applications of Machine Learning in Drug Discovery and Development,” Nature Reviews Drug Discovery 18, no. 6 (2019): 463–477.

[123] S. O'Sullivan, S. Leonard, A. Holzinger, et al., “Operational Framework and Training Standard Requirements for AI-Empowered Robotic Surgery,” International Journal of Medical Robotics and Computer Assisted Surgery 16, no. 5 (2020): 1–13.

[124] U. J. Muehlematter, P. Daniore, and K. N. Vokinger, “Approval of Artificial Intelligence and Machine Learning-Based Medical Devices in the USA and Europe (2015-20): A Comparative Analysis,” Lancet Digital Health 3, no. 3 (2021): e195–e203.

[125] D. Hendriks, A. Pagliaro, F. Andreatta, et al., “Human Fetal Brain Self-Organizes Into Long-Term Expanding Organoids,” Cell 187, no. 3 (2024): 712–732.e38.

[126] J. F. Dekkers, M. Alieva, L. M. Wellens, et al., “High-Resolution 3D Imaging of Fixed and Cleared Organoids,” Nature Protocols 14, no. 6 (2019): 1756–1771.

[127] X. Tang, Z. Wang, L. Deng, et al., “A Review of the Intelligent Optimization and Decision in Plastic Forming,” Materials 15, no. 19 (2022): 7019.

[128] M. X. Doss and A. Sachinidis, “Current Challenges of iPSC-Based Disease Modeling and Therapeutic Implications,” Cells 8, no. 5 (2019): 403.

[129] L. Bai, P. Song, and J. Su, “Bioactive Elements Manipulate Bone Regeneration,” Biomaterials Translational 4, no. 4 (2023): 248–269.

[130] X. Du, Z. Chen, Q. Li, et al., “Organoids Revealed: Morphological Analysis of the Profound Next Generation In-Vitro Model With Artificial Intelligence,” Bio-Design and Manufacturing 6, no. 3 (2023): 319–339.

[131] F. Wang, P. Song, J. Wang, et al., “Organoid Bioinks: Construction and Application,” Biofabrication 16, no. 3 (2024): 032006.

[132] R. Matsumoto, T. Yamamoto, and Y. Takahashi, “Complex Organ Construction From Human Pluripotent Stem Cells for Biological Research and Disease Modeling With New Emerging Techniques,” International Journal of Molecular Sciences 22, no. 19 (2021): 10184.

[133] G. Kouroupi, N. Antoniou, K. Prodromidou, E. Taoufik, and R. Matsas, “Patient-Derived Induced Pluripotent Stem Cell-Based Models in Parkinson's Disease for Drug Identification,” International Journal of Molecular Sciences 21, no. 19 (2020): 7113.

[134] B. Bhinder, C. Gilvary, N. S. Madhukar, and O. Elemento, “Artificial Intelligence in Cancer Research and Precision Medicine,” Cancer Discovery 11, no. 4 (2021): 900–915.

[135] K. B. Johnson, W. Q. Wei, D. Weeraratne, et al., “Precision Medicine, AI, and the Future of Personalized Health Care,” Clinical and Translational Science 14, no. 1 (2021): 86–93.

[136] X. Zhou, M. Qu, P. Tebon, et al., “Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence,” Advanced Science 7, no. 19 (2020): 2001447.

[137] Y. Rivenson, H. Wang, Z. Wei, et al., “Virtual Histological Staining of Unlabelled Tissue-Autofluorescence Images via Deep Learning,” Nature Biomedical Engineering 3, no. 6 (2019): 466–477.

[138] P. Mergenthaler, S. Hariharan, J. M. Pemberton, C. Lourenco, L. Z. Penn, and D. W. Andrews, “Rapid 3D Phenotypic Analysis of Neurons and Organoids Using Data-Driven Cell Segmentation-Free Machine Learning,” PLoS Computational Biology 17, no. 2 (2021): e1008630.

[139] X. Gui, A. Bazarova, R. Del Amor, et al., “PICaSSO Histologic Remission Index (PHRI) in Ulcerative Colitis: Development of a Novel Simplified Histological Score for Monitoring Mucosal Healing and Predicting Clinical Outcomes and Its Applicability in an Artificial Intelligence System,” Gut 71, no. 5 (2022): 889–898.

[140] S. Zare Harofte, M. Soltani, S. Siavashy, and K. Raahemifar, “Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment,” Small 18, no. 42 (2022): e2203169.

[141] R. Y. Lee, Y. Wu, D. Goh, et al., “Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment,” Advanced Healthcare Materials 12, no. 14 (2023): e2202457.

[142] T. Takebe, B. Zhang, and M. Radisic, “Synergistic Engineering: Organoids Meet Organs-on-a-Chip,” Cell Stem Cell 21, no. 3 (2017): 297–300.

[143] K. L. Fetah, B. J. DiPardo, E. M. Kongadzem, et al., “Cancer Modeling-on-a-Chip With Future Artificial Intelligence Integration,” Small 15, no. 50 (2019): e1901985.

[144] J. Li, J. Chen, H. Bai, et al., “An Overview of Organs-on-Chips Based on Deep Learning,” Research 2022 (2022): 9869518.

[145] Y. Wang and H. Jeon, “3D Cell Cultures Toward Quantitative High-Throughput Drug Screening,” Trends in Pharmacological Sciences 43, no. 7 (2022): 569–581.

[146] S. Deng, C. Li, J. Cao, et al., “Organ-on-a-Chip Meets Artificial Intelligence in Drug Evaluation,” Theranostics 13, no. 13 (2023): 4526–4558.

[147] J. Riordon, D. Sovilj, S. Sanner, D. Sinton, and E. W. K. Young, “Deep Learning With Microfluidics for Biotechnology,” Trends in Biotechnology 37, no. 3 (2019): 310–324.

[148] W. K. Huang, S. Z. H. Wong, S. R. Pather, et al., “Generation of Hypothalamic Arcuate Organoids From Human Induced Pluripotent Stem Cells,” Cell Stem Cell 28, no. 9 (2021): 1657–1670.e10.

[149] G. M. Anand, H. C. Megale, S. H. Murphy, et al., “Controlling Organoid Symmetry Breaking Uncovers an Excitable System Underlying Human Axial Elongation,” Cell 186, no. 3 (2023): 497–512.e23.

[150] S. K. Khanal, A. Tarafdar, and S. You, “Artificial Intelligence and Machine Learning for Smart Bioprocesses,” Bioresource Technology 375 (2023): 128826.

[151] C. J. Haug and J. M. Drazen, “Artificial Intelligence and Machine Learning in Clinical Medicine,” New England Journal of Medicine 388, no. 13 (2023): 1201–1208.

[152] Y. Le Cun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature 521, no. 7553 (2015): 436–444.

[153] A. Mencattini, D. Di Giuseppe, M. C. Comes, et al., “Discovering the Hidden Messages Within Cell Trajectories Using a Deep Learning Approach for In Vitro Evaluation of Cancer Drug Treatments,” Scientific Reports 10, no. 1 (2020): 7653.

[154] Z. Chen, N. Ma, X. Sun, et al., “Automated Evaluation of Tumor Spheroid Behavior in 3D Culture Using Deep Learning-Based Recognition,” Biomaterials 272 (2021): 120770.

[155] C. Yu and J. Jiang, “A Perspective on Using Machine Learning in 3D Bioprinting,” International Journal of Bioprinting 6, no. 1 (2020): 253.

[156] J. Ko, S. N. Baldassano, P. L. Loh, K. Kording, B. Litt, and D. Issadore, “Machine Learning to Detect Signatures of Disease in Liquid Biopsies—A User's Guide,” Lab on a Chip 18, no. 3 (2018): 395–405.

[157] J. Ko, N. Bhagwat, S. S. Yee, et al., “Combining Machine Learning and Nanofluidic Technology to Diagnose Pancreatic Cancer Using Exosomes,” ACS Nano 11, no. 11 (2017): 11182–11193.

[158] J. Kong, H. Lee, D. Kim, et al., “Network-Based Machine Learning in Colorectal and Bladder Organoid Models Predicts Anti-Cancer Drug Efficacy in Patients,” Nature Communications 11, no. 1 (2020): 5485.

[159] N. Goldhammer, J. Kim, V. Timmermans-Wielenga, and O. W. Petersen, “Characterization of Organoid Cultured Human Breast Cancer,” Breast Cancer Research 21, no. 1 (2019): 141.

[160] P. Ghosh, V. J. Campos, D. T. Vo, et al., “AI-Assisted Discovery of an Ethnicity-Influenced Driver of Cell Transformation in Esophageal and Gastroesophageal Junction Adenocarcinomas,” JCI Insight 7, no. 18 (2022): e161334.

[161] P. He, K. Lim, D. Sun, et al., “A Human Fetal Lung Cell Atlas Uncovers Proximal-Distal Gradients of Differentiation and Key Regulators of Epithelial Fates,” Cell 185, no. 25 (2022): 4841–4860.e25.

[162] S. Kanton, M. J. Boyle, Z. He, et al., “Organoid Single-Cell Genomic Atlas Uncovers Human-Specific Features of Brain Development,” Nature 574, no. 7778 (2019): 418–422.

[163] Z. He, A. Maynard, A. Jain, et al., “Lineage Recording in Human Cerebral Organoids,” Nature Methods 19, no. 1 (2022): 90–99.

[164] H. Wu, K. Uchimura, E. L. Donnelly, Y. Kirita, S. A. Morris, and B. D. Humphreys, “Comparative Analysis and Refinement of Human PSC-Derived Kidney Organoid Differentiation With Single-Cell Transcriptomics,” Cell Stem Cell 23, no. 6 (2018): 869–881.e8.

[165] P. Kadur Lakshminarasimha Murthy, V. Sontake, A. Tata, et al., “Human Distal Lung Maps and Lineage Hierarchies Reveal a Bipotent Progenitor,” Nature 604, no. 7904 (2022): 111–119.

[166] S. Ji, L. Feng, Z. Fu, et al., “Pharmaco-Proteogenomic Characterization of Liver Cancer Organoids for Precision Oncology,” Science Translational Medicine 15, no. 706 (2023): eadg3358.

[167] B. Paulsen, S. Velasco, A. J. Kedaigle, et al., “Autism Genes Converge on Asynchronous Development of Shared Neuron Classes,” Nature 602, no. 7896 (2022): 268–273.

[168] P. L. Hsu, C. W. Chien, Y. A. Tang, et al., “Targeting BRD3 Eradicates Nuclear TYRO3-Induced Colorectal Cancer Metastasis,” Science Advances 9, no. 15 (2023): eade3422.

[169] A. F. M. Dost, A. L. Moye, M. Vedaie, et al., “Organoids Model Transcriptional Hallmarks of Oncogenic KRAS Activation in Lung Epithelial Progenitor Cells,” Cell Stem Cell 27, no. 4 (2020): 663–678.e8.

[170] S. Kim, S. Min, Y. S. Choi, et al., “Tissue Extracellular Matrix Hydrogels as Alternatives to Matrigel for Culturing Gastrointestinal Organoids,” Nature Communications 13, no. 1 (2022): 1692.

[171] D. Rodrigues, T. de Souza, L. Coyle, et al., “New Insights Into the Mechanisms Underlying 5-Fluorouracil-Induced Intestinal Toxicity Based on Transcriptomic and Metabolomic Responses in Human Intestinal Organoids,” Archives of Toxicology 95, no. 8 (2021): 2691–2718.

[172] M. Y. Lu, T. Y. Chen, D. F. K. Williamson, et al., “AI-Based Pathology Predicts Origins for Cancers of Unknown Primary,” Nature 594, no. 7861 (2021): 106–110.

[173] I. Chiaradia, I. Imaz-Rosshandler, B. S. Nilges, et al., “Tissue Morphology Influences the Temporal Program of Human Brain Organoid Development,” Cell Stem Cell 30, no. 10 (2023): 1351–1367.e10.

[174] N. Sachs, J. de Ligt, O. Kopper, et al., “A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity,” Cell 172, no. 1–2 (2018): 373–386.e10.

[175] A. Albanese, J. M. Swaney, D. H. Yun, et al., “Multiscale 3D Phenotyping of Human Cerebral Organoids,” Scientific Reports 10, no. 1 (2020): 21487.

[176] T. Sharf, T. van der Molen, S. M. K. Glasauer, et al., “Functional Neuronal Circuitry and Oscillatory Dynamics in Human Brain Organoids,” Nature Communications 13, no. 1 (2022): 4403.

[177] J. Zhao, X. Wang, J. Zhu, et al., “PhaseFIT: Live-Organoid Phase-Fluorescent Image Transformation via Generative AI,” Light: Science & Applications 12, no. 1 (2023): 297.

[178] F. Jacob, R. D. Salinas, D. Y. Zhang, et al., “A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-Tumoral Heterogeneity,” Cell 180, no. 1 (2020): 188–204.e22.

[179] A. A. Mansour, J. T. Gonçalves, C. W. Bloyd, et al., “An In Vivo Model of Functional and Vascularized Human Brain Organoids,” Nature Biotechnology 36, no. 5 (2018): 432–441.

[180] P. J. Tebon, B. Wang, A. L. Markowitz, et al., “Drug Screening at Single-Organoid Resolution via Bioprinting and Interferometry,” Nature Communications 14, no. 1 (2023): 3168.

[181] J. M. Matthews, B. Schuster, S. S. Kashaf, et al., “OrganoID: A Versatile Deep Learning Platform for Tracking and Analysis of Single-Organoid Dynamics,” PLoS Computational Biology 18, no. 11 (2022): e1010584.

[182] C. Deben, E. C. De La Hoz, M. L. Compte, et al., “OrBITS: Label-Free and Time-Lapse Monitoring of Patient Derived Organoids for Advanced Drug Screening,” Cellular Oncology 46, no. 2 (2023): 299–314.

[183] J. Roper, T. Tammela, N. M. Cetinbas, et al., “In Vivo Genome Editing and Organoid Transplantation Models of Colorectal Cancer and Metastasis,” Nature Biotechnology 35, no. 6 (2017): 569–576.

[184] M. J. Mosquera, S. Kim, R. Bareja, et al., “Extracellular Matrix in Synthetic Hydrogel-Based Prostate Cancer Organoids Regulate Therapeutic Response to EZH2 and DRD2 Inhibitors,” Advanced Materials 34, no. 2 (2022): e2100096.

[185] M. Hemberger and W. Dean, “The Placenta: Epigenetic Insights into Trophoblast Developmental Models of a Generation-Bridging Organ With Long-Lasting Impact on Lifelong Health,” Physiological Reviews 103, no. 4 (2023): 2523–2560.

[186] K. J. Howell, J. Kraiczy, K. M. Nayak, et al., “DNA Methylation and Transcription Patterns in Intestinal Epithelial Cells From Pediatric Patients With Inflammatory Bowel Diseases Differentiate Disease Subtypes and Associate With Outcome,” Gastroenterology 154, no. 3 (2018): 585–598.

[187] R. E. Niec, T. Chu, M. Schernthanner, et al., “Lymphatics Act as a Signaling Hub to Regulate Intestinal Stem Cell Activity,” Cell Stem Cell 29, no. 7 (2022): 1067–1082.e18.

[188] L. M. Pikkupeura, R. B. Bressan, J. Guiu, et al., “Transcriptional and Epigenomic Profiling Identifies Yap Signaling as a Key Regulator of Intestinal Epithelium Maturation,” Science Advances 9, no. 28 (2023): eadf9460.

[189] J. Beumer, J. Puschhof, J. Bauzá-Martinez, et al., “High-Resolution mRNA and Secretome Atlas of Human Enteroendocrine Cells,” Cell 181, no. 6 (2020): 1291–1306.e19.

[190] S. Y. Cho, H. Hwang, Y. H. Kim, et al., “Refining Classification of Cholangiocarcinoma Subtypes via Proteogenomic Integration Reveals New Therapeutic Prospects,” Gastroenterology 164, no. 7 (2023): 1293–1309.

[191] R. R. J. Low, K. Y. Fung, H. Gao, et al., “S100 Family Proteins Are Linked to Organoid Morphology and EMT in Pancreatic Cancer,” Cell Death & Differentiation 30, no. 5 (2023): 1155–1165.

[192] M. Kimura, T. Iguchi, K. Iwasawa, et al., “En masse Organoid Phenotyping Informs Metabolic-Associated Genetic Susceptibility to NASH,” Cell 185, no. 22 (2022): 4216–4232.e16.

[193] Y. Xiao, D. Ma, Y. S. Yang, et al., “Comprehensive Metabolomics Expands Precision Medicine for Triple-Negative Breast Cancer,” Cell Research 32, no. 5 (2022): 477–490.

[194] J. Chen, S. Ma, H. Yang, et al., “Generation and Metabolomic Characterization of Functional Ductal Organoids With Biliary Tree Networks in Decellularized Liver Scaffolds,” Bioactive Materials 26 (2023): 452–464.

[195] R. Wang, Y. Mao, W. Wang, et al., “Systematic Evaluation of Colorectal Cancer Organoid System by Single-Cell RNA-Seq Analysis,” Genome Biology 23, no. 1 (2022): 106.

[196] C. Li, J. S. Fleck, C. Martins-Costa, et al., “Single-Cell Brain Organoid Screening Identifies Developmental Defects in Autism,” Nature 621, no. 7978 (2023): 373–380.

[197] C. S. Cowan, M. Renner, M. De Gennaro, et al., “Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution,” Cell 182, no. 6 (2020): 1623–1640.e34.

[198] J. M. Chan, S. Zaidi, J. R. Love, et al., “Lineage Plasticity in Prostate Cancer Depends on JAK/STAT Inflammatory Signaling,” Science 377, no. 6611 (2022): 1180–1191.

[199] S. Raghavan, P. S. Winter, A. W. Navia, et al., “Microenvironment Drives Cell State, Plasticity, and Drug Response in Pancreatic Cancer,” Cell 184, no. 25 (2021): 6119–6137.e26.

[200] L. Garcia-Alonso, L. F. Handfield, K. Roberts, et al., “Mapping the Temporal and Spatial Dynamics of the Human Endometrium In Vivo and In Vitro,” Nature Genetics 53, no. 12 (2021): 1698–1711.

[201] C. A. Herring, R. K. Simmons, S. Freytag, et al., “Human Prefrontal Cortex Gene Regulatory Dynamics From Gestation to Adulthood at Single-Cell Resolution,” Cell 185, no. 23 (2022): 4428–4447.e28.

[202] E. Rondonotti, C. Hassan, G. Tamanini, et al., “Artificial Intelligence-Assisted Optical Diagnosis for the Resect-and-Discard Strategy in Clinical Practice: The Artificial Intelligence BLI Characterization (ABC) Study,” Endoscopy 55, no. 1 (2023): 14–22.

[203] J. Shao, J. Ma, Q. Zhang, W. Li, and C. Wang, “Predicting Gene Mutation Status via Artificial Intelligence Technologies Based on Multimodal Integration (MMI) to Advance Precision Oncology,” Seminars in Cancer Biology 91 (2023): 1–15.

[204] A. Repici, M. Spadaccini, G. Antonelli, et al., “Artificial Intelligence and Colonoscopy Experience: Lessons From Two Randomised Trials,” Gut 71, no. 4 (2022): 757–765.

[205] R. J. Chen, M. Y. Lu, D. F. K. Williamson, et al., “Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning,” Cancer Cell 40, no. 8 (2022): 865–878.e6.

[206] T. Hollon, C. Jiang, A. Chowdury, et al., “Artificial-Intelligence-Based Molecular Classification of Diffuse Gliomas Using Rapid, Label-Free Optical Imaging,” Nature Medicine 29, no. 4 (2023): 828–832.

[207] A. Crombé, M. Roulleau-Dugage, and A. Italiano, “The Diagnosis, Classification, and Treatment of Sarcoma in This Era of Artificial Intelligence and Immunotherapy,” Cancer Communications 42, no. 12 (2022): 1288–1313.

[208] H. M. Vergara, C. Pape, K. I. Meechan, et al., “Whole-Body Integration of Gene Expression and Single-Cell Morphology,” Cell 184, no. 18 (2021): 4819–4837.e22.

[209] F. Endo, A. Kasai, J. S. Soto, et al., “Molecular Basis of Astrocyte Diversity and Morphology Across the CNS in Health and Disease,” Science 378, no. 6619 (2022): eadc9020.

[210] H. Peng, P. Xie, L. Liu, et al., “Morphological Diversity of Single Neurons in Molecularly Defined Cell Types,” Nature 598, no. 7879 (2021): 174–181.

[211] J. Barnes, M. Brendel, V. R. Gao, et al., “A Non-Invasive Artificial Intelligence Approach for the Prediction of Human Blastocyst Ploidy: A Retrospective Model Development and Validation Study,” Lancet Digital Health 5, no. 1 (2023): e28–e40.

[212] A. Mavropoulos, C. Johnson, V. Lu, et al., “Artificial Intelligence-Driven Morphology-Based Enrichment of Malignant Cells From Body Fluid,” Modern Pathology 36, no. 8 (2023): 100195.

[213] M. Selig, L. Poehlman, N. C. Lang, et al, “Prediction of Six Macrophage Phenotypes and Their IL-10 Content Based on Single-Cell Morphology Using Artificial Intelligence,” Frontiers in immunology 14 (2023): 1336393.

[214] B. Shoukri, J. C. Prieto, A. Ruellas, et al., “Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis,” Journal of Dental Research 98, no. 10 (2019): 1103–1111.

[215] D. Unnersjö-Jess, L. Butt, M. Höhne, et al., “Deep Learning-Based Segmentation and Quantification of Podocyte Foot Process Morphology Suggests Differential Patterns of Foot Process Effacement Across Kidney Pathologies,” Kidney International 103, no. 6 (2023): 1120–1130.

[216] S. Sart, R. F. X. Tomasi, A. Barizien, G. Amselem, A. Cumano, and C. N. Baroud, “Mapping the Structure and Biological Functions Within Mesenchymal Bodies Using Microfluidics,” Science Advances 6, no. 10 (2020): eaaw7853.

[217] S. T. Moore, T. Nakamura, J. Nie, et al., “Generating High-Fidelity Cochlear Organoids From Human Pluripotent Stem Cells,” Cell Stem Cell 30, no. 7 (2023): 950–961.e7.

[218] R. K. Gurumurthy, S. Koster, N. Kumar, T. F. Meyer, and C. Chumduri, “Patient-Derived and Mouse Endo-Ectocervical Organoid Generation, Genetic Manipulation and Applications to Model Infection,” Nature Protocols 17, no. 7 (2022): 1658–1690.

[219] Y. Wang, R. K. Kankala, J. Zhang, et al., “Modeling Endothelialized Hepatic Tumor Microtissues for Drug Screening,” Advanced Science 7, no. 21 (2020): 2002002.

[220] B. Subia, U. R. Dahiya, S. Mishra, et al., “Breast Tumor-on-Chip Models: From Disease Modeling to Personalized Drug Screening,” Journal of Controlled Release 331 (2021): 103–120.

[221] R. Roberts, “Mendelian Randomization Studies Promise to Shorten the Journey to FDA Approval,” JACC: Basic to Translational Science 3, no. 5 (2018): 690–703.

[222] B. Schuster, M. Junkin, S. S. Kashaf, et al., “Automated Microfluidic Platform for Dynamic and Combinatorial Drug Screening of Tumor Organoids,” Nature Communications 11, no. 1 (2020): 5271.

[223] G. Fang, H. Lu, L. Rodriguez de la Fuente, et al., “Mammary Tumor Organoid Culture in Non-Adhesive Alginate for Luminal Mechanics and High-Throughput Drug Screening,” Advanced Science 8, no. 21 (2021): e2102418.

[224] C. Calandrini, S. R. van Hooff, I. Paassen, et al., “Organoid-Based Drug Screening Reveals Neddylation as Therapeutic Target for Malignant Rhabdoid Tumors,” Cell Reports 36, no. 8 (2021): 109568.

[225] A. Gangwal, A. Ansari, I. Ahmad, et al., “Generative Artificial Intelligence in Drug Discovery: Basic Framework, Recent Advances, Challenges, and Opportunities,” Frontiers in Pharmacology 15 (2024): 1331062.

[226] S. Kumari, R. Gupta, R. K. Ambasta, and P. Kumar, “Multiple Therapeutic Approaches of Glioblastoma Multiforme: From Terminal to Therapy,” Biochimica et Biophysica Acta (BBA)—Reviews on Cancer 1878, no. 4 (2023): 188913.

[227] L. Smirnova and T. Hartung, “The Promise and Potential of Brain Organoids,” Advanced Healthcare Materials 13, no. 21 (2024): e2302745.

[228] M. Le Compte, E. C. De La Hoz, S. Peeters, et al., “Single-Organoid Analysis Reveals Clinically Relevant Treatment-Resistant and Invasive Subclones in Pancreatic Cancer,” npj Precision Oncology 7, no. 1 (2023): 128.

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