[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127037-en":3,"doc-seo-127037-105":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},127037,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A machine learning approach for quantifying age-related histological changes in the mouse kidney - research paper","Quantifying aging-related changes in histological samples is essential for assessing interventions that aim to extend health span. This study develops a machine learning architecture trained to detect and quantify age-associated histological alterations in mouse kidney tissue using held-out data. Model performance is validated and shown to correlate with pathologist scores from the Geropathology Research Network aging grading scheme. The approach yields reproducible, measurable age scores and enables trained classifiers and tutorials for H&E slides and extension to other stains and tissues.","The Jackson Laboratory  \nThe Mouseion at the JAXlibrary  \n\n| Faculty Research 2024 | Faculty & Staff Research |\n| --- | --- |\n\n4-1-2024  \nA machine learning approach for quantifying age-related histological changes in the mouse kidney.  \nSusan Sheehan  \nSeamus Mawe Mandy Chen Jenna Klug Warren Ladiges  \nSee next page for additional authors  \nFollow this and additional works at: [https://mouseion.jax.org/stfb2024](https://mouseion.jax.org/stfb2024)  \nAuthors  \nSusan Sheehan, Seamus Mawe, Mandy Chen, Jenna Klug, Warren Ladiges, Ron Korstanje, and J Matthew Mahoney  \nGeroScience (2024) 46:2571–2581  \n[https://doi.org/10.1007/s11357-023-01013-y](https://doi.org/10.1007/s11357-023-01013-y)  \nA machine learning approach for quantifying age‑related histological changes in the mouse kidney  \nSusan Sheehan · Seamus Mawe · Mandy Chen ·  \nJenna Klug · Warren Ladiges · Ron Korstanje ·  \nJ. Matthew Mahoney  \nReceived: 10 July 2023 / Accepted: 7 November 2023 / Published online: 16 December 2023 © The Author(s) 2023  \nAbstract The ability to quantify aging-related changes in histological samples is important, as it allows for evaluation of interventions intended to effect health span. We used a machine learning architecture that can be trained to detect and quantify these changes in the mouse kidney. Using additional held out data, we show validation of our model, correlation with scores given by pathologists using the Geropathology Research Network aging grading scheme, and its application in providing reproducible and quantifiable age scores for histological samples. Aging quantification also provides the insights into possible changes in image appearance that are independent of specific geropathology-specified lesions. Furthermore, we provide trained classifiers for H&E-stained slides, as well as tutorials on how to use these and how to create additional classifiers for other histological stains and tissues  \nSupplementary Information The online version contains supplementary material available at [https://doi](https://doi). org/10.1007/s11357-023-01013-y.  \nS. Sheehan · S. Mawe · M. Chen · R. Korstanje ·  \nJ. M. Mahoney (*)  \nThe Jackson Laboratory, Bar Harbor, ME 04609, USA [e-mail: matt.mahoney@jax.org](e-mail: matt.mahoney@jax.org)  \n[J. Klug](J. Klug) · W. Ladiges  \nDepartment of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA, USA  \nJ. M. Mahoney  \nDepartment of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, VT, USA  \nusing our architecture. This architecture and combined resources allow for the high throughput quantification of mouse aging studies in general and specifically applicable to kidney tissues.  \nKeywords Machine learning approach · Mouse kidney · Histological evaluation  \nIntroduction  \nHistological evaluation is often the first stop for diagnosis of many diseases, but pathologists rarely define age-related changes in their reports, despite age being the greatest risk factor for many conditions. The field of geropathology is focused on classifying age-related changes that occur and can be visualized in histological samples. The Geropathology Research Network (GRN; see Supplemental Table 1) recently published an aging grading scheme for multiple mouse tissues, including the kidney [1], and this system has been shown to be effective for quantifying the effects of interventions [2] . The GRN scoring system has yet tobe widely adopted, partially due to its novelty. However, a major limitation for implementing this grading scheme at scale is low throughput caused by limited access to trained pathologists that can process the large numbers of slides that are involved in a typical study. Furthermore, pathologist judgments are a combination of objective and subjective impressions of extremely complex visual patterns, which can result in variability  \namong pathologists and low concordance of final pathological scores. Indeed, concordance of GRN scores as low","cbCaiegVOGYK3gHI","https://ap.wps.com/l/cbCaiegVOGYK3gHI","pdf",1155715,1,13,"English","en",105,"# Abstract\n## Model validation and correlation\n## Reproducible age scoring\n## Image-independent insights and classifiers\n# Introduction\n## Limits of histological aging reporting\n## Geropathology Research Network grading constraints\n## Need for scalable, annotation-light quantification","[{\"question\":\"What problem does the machine learning approach address?\",\"answer\":\"It addresses the difficulty of quantifying age-related histological changes at scale, where expert pathologist throughput and scoring variability limit consistent evaluation.\"},{\"question\":\"How is the model validated in the study?\",\"answer\":\"Validation uses held-out data and shows correlation with pathologist scores based on the Geropathology Research Network aging grading scheme.\"},{\"question\":\"What outputs does the approach provide for researchers?\",\"answer\":\"It provides reproducible and quantifiable age scores for histological samples and trained classifiers for H\\u0026E-stained slides, along with tutorials to build additional classifiers for other stains and tissues.\"}]","A machine learning approach for quantifying age-related histological changes in the mouse kidney - 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