ArticleThe American journal of pathology2021
Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.
Article in The American journal of pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Association Between Microscopic Hematuria and Proteinuria Remission in Adult Nephrotic Syndrome.Kidney medicine · 2026Article
- A deep learning and morphometric hybrid model for automated quantification of kidney interstitial fibrosis in trichrome-stained whole-slide image.Journal of pathology informatics · 2026Article
- Quantification of interstitial fibrosis in digitized kidney biopsies using deep neural networks.Diagnostic pathology · 2026Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.Scientific reports · 2025Article
- Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.Current opinion in organ transplantation · 2025Review
- Ecologically sustainable benchmarking of AI models for histopathology.NPJ digital medicine · 2024Article
- Smartphone-based machine learning model for real-time assessment of medical kidney biopsy.Journal of pathology informatics · 2024Article
- Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.Renal failure · 2024Article
- Clinical relevance of proteinuria selectivity index and fractional excretion of sodium in patients with nephrotic syndrome.Scientific reports · 2024Article
- Spatial transcriptomics in health and disease.Nature reviews. Nephrology · 2024Review
- [Artificial intelligence in kidney transplant pathology].Pathologie (Heidelberg, Germany) · 2024Review
- PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.The Lancet. Digital health · 2024Article
- Reconfiguration and loss of peritubular capillaries in chronic kidney disease.Scientific reports · 2023Article
- Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation.IEEE transactions on bio-medical engineering · 2023Article
- Learning more from the inter-rater reliability of interstitial fibrosis assessment beyond just a statistic.Scientific reports · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- A Graph-Transformer for Whole Slide Image Classification.IEEE transactions on medical imaging · 2022Article
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12 authors.
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Abstract
Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.
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