[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-211491-en":3,"doc-seo-211491-105":30,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},211491,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Prediction of the Hypothalamus-Pituitary Organoid Formation Using Machine Learning","Matsumoto and colleagues develop a machine learning model that predicts successful hypothalamus-pituitary organoid induction from human iPSCs using only phase-contrast images collected at early differentiation stages. The model improves induction efficiency by identifying morphological characteristics on the organoid surface as key predictors. Using images from organoids at day 9, the approach forecasts pituitary cell differentiation at day 40 with an accuracy of 79%. This enables faster, more consistent organoid experiments and supports investigation of molecular mechanisms underlying hypothalamus-pituitary differentiation.","Article  \nPrediction of the hypothalamus-pituitary organoid formation using machine learning  \nAuthors  \nRyusaku Matsumoto, Hidetaka Suga, Yutaka Takahashi, Takashi Aoi, Takuya Yamamoto  \nCorrespondence  \n[r.matsumoto@cira.kyoto-u.ac.jp](r.matsumoto@cira.kyoto-u.ac.jp) (R.M.),  \n[takuya@cira.kyoto-u.ac.jp](takuya@cira.kyoto-u.ac.jp) (T.Y.)  \nIn brief  \nMatsumoto et al. establish a machine learning-based model capable of predicting the successful hypothalamuspituitary organoid induction from human iPSCs based solely on phase-contrast images captured at early timing of differentiation.  \n• The model outperforms human researchers and can be applied to other cell lines  \n• The model makes predictions based on morphological features of the organoid surface  \n• The morphological change mirrors the cellular localization pattern within the organoid  \nMatsumoto et al., 2025, Cell Reports Methods 5, 101119  \nAugust 18, 2025 © 2025 The Author(s) . Published by Elsevier Inc.  \n[https://doi.org/10.1016/j.crmeth.2025.101119](https://doi.org/10.1016/j.crmeth.2025.101119)  \nll  \nll  \nOPEN ACCESS  \nArticle  \nPrediction of the hypothalamus-pituitary organoid formation using machine learning  \nRyusaku Matsumoto,1,2,3,* Hidetaka Suga,4 Yutaka Takahashi,2,5 Takashi Aoi,3 and Takuya Yamamoto1,6,7,8,*  \n1Center for iPS Cell Research and Application, Kyoto University, Kyoto 606-8507, Japan  \n2Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan  \n3Division of Stem Cell Medicine, Kobe University Graduate School of Medicine, Kobe, Hyogo 650-0017, Japan  \n4Department of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8560, Japan  \n5Department of Diabetes and Endocrinology, Nara Medical University, Kashihara, Nara 634-8522, Japan  \n6Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan  \n7Medical-Risk Avoidance Based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo 103-0027, Japan  \n8Lead contact  \n*Correspondence: [r.matsumoto@cira.kyoto-u.ac.jp](r.matsumoto@cira.kyoto-u.ac.jp) (R. M.), [takuya@cira.kyoto-u.ac.jp](takuya@cira.kyoto-u.ac.jp) (T.Y.) [https://doi.org/10.1016/j.crmeth.2025.101](https://doi.org/10.1016/j.crmeth.2025.101)119  \nMOTIVATION Organoid induction methods from pluripotent stem cells typically involve prolonged culture periods and often suffer from unstable induction efficiency, which hinders experimental productivity and widespread application. To address these problems, we developed a machine learning-based model capable of predicting the induction efficiency of the hypothalamus-pituitary organoids using only phasecontrast images of the early stages of their differentiation.  \nSUMMARY  \nMulti-cellular organoids are self-assembly aggregates that mimic biological functions and developmental processes of many tissue types in vitro. They are widely employed for disease modeling and functional studies. Hypothalamus-pituitary organoids can be generated through differentiation induction from pluripotent stem cells. However, their maturation is time consuming and labor intensive, and the quality of the resulting organoids can vary. Here, we developed a machine learning model capable of accurately predicting the successful generation of high-quality hypothalamus-pituitary organoids based solely on phase-contrast images captured during the early stage of differentiation. The model achieved an accuracy of 79% using images from organoids on day 9 to predict pituitary cell differentiation at day 40. Moreover, the computational approach identified the shape of the organoid surface as a critical determining factor that significantly affected the prediction. This model can help to enhance the efficiency of organoid induction experiments and illuminate the molecular mechanisms involved in hypothalamus-pituitary differentiation.  \nINTROD","cbCaiglxhaeE9Vew","https://ap.wps.com/l/cbCaiglxhaeE9Vew","pdf",12564011,1,20,"English","en",105,"# Motivation\n## Model approach and prediction target\n# Summary\n## Organoid context and performance\n## Key morphological determinant\n# Introduction\n## Biological importance of the hypothalamus-pituitary axis\n## Challenges with variability and time costs\n## Need for reliable models","[{\"question\":\"What data does the model use to predict organoid induction success?\",\"answer\":\"It uses phase-contrast images captured during the early stage of differentiation.\"},{\"question\":\"What prediction does the model make, and when?\",\"answer\":\"Using organoid images on day 9, it predicts pituitary cell differentiation at day 40.\"},{\"question\":\"Why does this approach help researchers?\",\"answer\":\"It improves induction efficiency and consistency, reducing time and monetary costs, while supporting study of mechanisms in hypothalamus-pituitary differentiation.\"}]","Prediction of the Hypothalamus-Pituitary Organoid Formation Using Machine Learning | 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