ArticleJournal of pathology informatics2022
Automated recognition of glomerular lesions in the kidneys of mice by using deep learning.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Glo-In-One-v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: an overview of recent publications about AI pathology.Journal of toxicologic pathology · 2025Review
- Small molecule APOL1 inhibitors as a precision medicine approach for APOL1-mediated kidney disease.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as much as in clinical settings. We have been performing automated recognition of various pathological animal tissues and quantitative analysis of pathological findings, including liver and lung. In this study, we attempted to construct an artificial intelligence (AI)-based trained model that can automatedly recognize glomerular lesions in mouse kidneys that are characterized by complex structures. Materials and methods: By using hematoxylin and eosin (HE)-stained whole slide images (WSI) from Results: Glomerular lesions (including mesangial proliferation, crescent formation, and sclerosis) observed in Col4a3 KO mice and ADR mice were detected by our trained model. The number of glomerular lesions detected by our trained model were also highly correlated with that of counted by pathologists. Conclusion: In this study, we constructed a trained model allowing us to automatedly recognize glomerular lesions in the mouse kidney with the use of the HALO AI system. The findings and insights of this study will facilitate the development of digital pathology in non-clinical research and improve the probability of success in drug discovery research.
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Registered trials
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