ReviewCurrent opinion in nephrology and hypertension2017
Digital pathology in nephrology clinical trials, research, and pathology practice.
Review in Current opinion in nephrology and hypertension, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed.
- Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: a multicentre model development and validation study.EClinicalMedicine · 2025Article
- Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease.medRxiv : the preprint server for health sciences · 2025Article
- Prediction model for renal outcomes in Latin American Mestizo patients with pure proliferative lupus nephritis.Lupus science & medicine · 2025Article
- Validation of a whole slide image management system for metabolic-associated steatohepatitis for clinical trials.The journal of pathology. Clinical research · 2024Article
- Characterization of arteriosclerosis based on computer-aided measurements of intra-arterial thickness.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence.Open life sciences · 2024Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in surgery: A research team perspective.Current problems in surgery · 2022Article
- Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2022Observational
- Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.Scientific reports · 2022Article
- Cellular and molecular interrogation of kidney biopsy specimens.Current opinion in nephrology and hypertension · 2022Review
- [Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2021Article
- Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.Kidney international · 2021Article
- Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association.Journal of pathology informatics · 2021Article
- Digital pathology and computational image analysis in nephropathology.Nature reviews. Nephrology · 2020Review
- Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma.Diagnostic pathology · 2020Article
- Artificial intelligence in nephropathology.Nature reviews. Nephrology · 2020Article
- Extracellular Matrix in Kidney Fibrosis: More Than Just a Scaffold.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2019Review
- The use of digital pathology and image analysis in clinical trials.The journal of pathology. Clinical research · 2019Article
- CureGN Study Rationale, Design, and Methods: Establishing a Large Prospective Observational Study of Glomerular Disease.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2019Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
purpose of reviewIn this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. RECENT
findingsRecent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases. SUMMARY: The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.
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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.