ArticleThe American journal of pathology2019
Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks.
Article in The American journal of pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 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
- Neural network analysis as a novel skin outcome in a trial of belumosudil in patients with systemic sclerosis.Arthritis research & therapy · 2025Article
- Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients.Renal failure · 2024Article
- A machine learning approach for quantifying age-related histological changes in the mouse kidney.GeroScience · 2024Article
- Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.Bioinformatics (Oxford, England) · 2024Article
- Artificial intelligence in renal pathology: Current status and future.Biomolecules & biomedicine · 2023Review
- Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?European radiology · 2022Article
- Artificial intelligence in glomerular diseases.Pediatric nephrology (Berlin, Germany) · 2022Review
- Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet.Frontiers in oncology · 2022Article
- Article
- How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?Seminars in immunopathology · 2021Review
- Article
- Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.Kidney international reports · 2021Article
- Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.The Journal of pathology · 2020Article
- High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.Arthritis research & therapy · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly quantified. We used DNN-based transfer learning to analyze histologic images of periodic acid-Schiff-stained renal sections from a cohort of mice with different genotypes. We demonstrate that DNN-based machine learning has strong generalization performance on multiple histologic image processing tasks. The neural network extracted quantitative image features and used them as classifiers to look for differences between mice of different genotypes. Excellent performance was observed at segmenting glomeruli from non-glomerular structure and subsequently predicting the genotype of the animal on the basis of glomerular quantitative image features. The DNN-based genotype classifications highly correlate with mesangial matrix expansion scored by a pathologist (R.E.C.), which differed in these animals. In addition, by analyzing non-glomeruli images, the neural network identified novel histologic features that differed by genotype, including the presence of vacuoles, nuclear count, and proximal tubule brush border integrity, which was validated with immunohistologic staining. These features were not identified in systematic pathologic examination. Our study demonstrates the power of DNNs to extract biologically relevant phenotypes and serve as a platform for discovering novel phenotypes. These results highlight the synergistic possibilities for pathologists and DNNs to radically scale up our ability to generate novel mechanistic hypotheses in disease.
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