ReviewCurrent opinion in nephrology and hypertension2020
Artificial intelligence driven next-generation renal histomorphometry.
Review in Current opinion in nephrology and hypertension, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unbiased self supervised learning of kidney histology reveals phenotypic and prognostic insights.Scientific reports · 2025Article
- In silico medicine and -omics strategies in nephrology: contributions and relevance to the diagnosis and prevention of chronic kidney disease.Kidney research and clinical practice · 2025Article
- Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies.Scientific reports · 2024Article
- Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool.Kidney international reports · 2022Article
- Artificial intelligence: is there a potential role in nephropathology?Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2022Article
- A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.Communications medicine · 2022Article
- Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease.International journal of environmental research and public health · 2021Article
- Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.The American journal of pathology · 2021Article
- AI applications in renal pathology.Kidney international · 2021Review
- Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.Histopathology · 2021Review
- User friendly, cloud based, whole slide image segmentation.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.Kidney international · 2021Article
- Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.Journal of clinical medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
purpose of reviewSuccessful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications. RECENT
findingsThe primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge. SUMMARY: Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.