[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121170-en":3,"doc-seo-121170-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121170,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning in Time-Lapse Imaging To Differentiate Embryos From Young vs Old Mice - Article","Time-lapse microscopy provides a non-invasive way to characterize early embryo development, and this study integrates machine learning to define how maternal aging alters embryonic growth kinetics. Continuous imaging was used to extract morphokinetic parameters from embryos of young and aged C57BL6/NJ mice. Aged embryos showed faster progression through cleavage to morula, with no major differences during later blastulation stages. Unsupervised clustering separated embryos by donor age, and supervised extreme gradient boosting predicted the age-related phenotype after tuning.","The Texas Medical Center Library  \nDigitalCommons@TMC  \n\n| Faculty and Staff Publications | Baylor College of Medicine |\n| --- | --- |\n| 6-12-2024\u003Cbr>Machine Learning in Time-Lapse Imaging To Differentiate Embryos From Young vs Old Mice†\u003Cbr>Liubin Yang Carolina Leynes Ashley Pawelka Isabel Lorenzo\u003Cbr>Andrew Chou\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://digitalcommons.library.tmc.edu/baylor_docs](https://digitalcommons.library.tmc.edu/baylor_docs)\u003Cbr> Part of the Biological Phenomena, Cell Phenomena, and Immunity Commons, Biomedical Informatics Commons, Genetics and Genomics Commons, Medical Genetics Commons, Medical Molecular Biology Commons, and the Medical Specialties Commons |  |\n\nRecommended Citation  \nYang, Liubin; Leynes, Carolina; Pawelka, Ashley; Lorenzo, Isabel; Chou, Andrew; Lee, Brendan; and Heaney, Jason D, \"Machine Learning in Time-Lapse Imaging To Differentiate Embryos From Young vs Old Mice†\"(2024) . Faculty and Staff Publications. 2183.  \n[https://digitalcommons.library.tmc.edu/baylor_docs/2183](https://digitalcommons.library.tmc.edu/baylor_docs/2183)  \nThis Article is brought to you for free and open access by the Baylor College of Medicine at  \nDigitalCommons@TMC. It has been accepted for inclusion in Faculty and Staff Publications by an authorized administrator of DigitalCommons@TMC. For more information, please contact [digcommons@library.tmc.edu](digcommons@library.tmc.edu).  \nAuthors  \nLiubin Yang, Carolina Leynes, Ashley Pawelka, Isabel Lorenzo, Andrew Chou, Brendan Lee, and Jason D Heaney  \nThis article is available at DigitalCommons@TMC: [https://digitalcommons.library.tmc.edu/baylor_docs/2183](https://digitalcommons.library.tmc.edu/baylor_docs/2183)  \nMachine learning in time-lapse imaging to differentiate embryos from young vs old mice†  \nLiubin Yang1 ,2 ,3 , * , Carolina Leynes3 , Ashley Pawelka3 , Isabel Lorenzo3 , Andrew Chou4 ,5 , Brendan Lee3 and Jason D. Heaney3  \n1 Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, USA  \n2 Division of Reproductive Endocrinology and Infertility, Division of Reproductive Sciences, Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut, USA  \n3 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA  \n4 Pain Research, Informatics, Multi-morbidities, and Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, Connecticut, USA  \n5 Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA  \n*Correspondence: Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, 310 Cedar Street, LSOG 305A, New Haven, CT 06510, USA. Tel: 203-737-5674; Fax: 203-785-2514; E-mail: [Liubin.Yang@yale.edu](Liubin.Yang@yale.edu)  \n†Grant Support: This work was supported by the National Human Genome Research Institute/National Institutes of Health (UM1 HG006348 to JDH), by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (grant 5K12HD047018 to LY), and by the Baylor College of Medicine Department of Obstetrics and Gynecology internal fellowship grant (to LY) . The project described was also supported by a Career Development Award from the American Society of Gene & Cell Therapy (to LY) . The content is solely the responsibility of the authors and does not necessarily represent the official views of the American Society of Gene & Cell Therapy. This research was also supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Clinical Science Research and Development (VA CSRD grant no. IK2 CX001981 to AC) and Health Services Research and Development (\\#CIN 13-407) . The views expressed in this manuscript are those of the authors and do not necessarily reflect th","cbCaifJboNN5cQqh","https://ap.wps.com/l/cbCaifJboNN5cQqh","pdf",1295232,1,12,"English","en",105,"# Abstract\n## Study design and imaging\n## Machine-learning analysis and results\n## Key findings and potential applications","[{\"question\":\"How does the study use time-lapse microscopy to assess embryo development?\",\"answer\":\"Embryos from young and aged mice are continuously imaged, and morphokinetic parameters are extracted to track developmental stage transitions.\"},{\"question\":\"What differences were observed between embryos from young versus aged donors?\",\"answer\":\"Aged embryos accelerated through cleavage stages from 5-cells to morula, while later blastulation stages showed no significant differences.\"},{\"question\":\"Which machine-learning approach predicted the maternal-age phenotype, and how well did it perform?\",\"answer\":\"Supervised extreme gradient boosting predicted the age-related phenotype with an accuracy of 0.78, precision of 0.81, and recall of 0.83 after hyperparameter tuning.\"}]","Machine Learning in Time-Lapse Imaging To Differentiate Embryos From Young vs Old Mice - 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