SynthesisJournal of nephrology2022
Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.
Synthesis in Journal of nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions.Journal of nephrology · 2024Pooled it
- Development and validation of urinary exosomal microRNA biomarkers for the diagnosis of acute rejection in kidney transplant recipients.Frontiers in immunology · 2023Pooled it
- Machine Learning for Monoclonal Gammopathies of Renal Significance Risk Stratification Using Clinical and Pathology Data.Kidney international reports · 2025Article
- Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies.Journal of nephrology · 2025Article
- Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.Journal of imaging informatics in medicine · 2025Article
- Article
- Deep learning-based histopathological assessment of tubulo-interstitial injury in chronic kidney diseases.Communications medicine · 2025Article
- AI assistance in aesthetic medicine-A consensus on objective medical standards.Journal of cosmetic dermatology · 2024Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Digital pathology structure and deployment in Veneto: a proof-of-concept study.Virchows Archiv : an international journal of pathology · 2024Article
- Kidney transplants from elderly donors: what we have learned 20 years after the Crystal City consensus criteria meeting.Journal of nephrology · 2024Review
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Cutting-edge technology and automation in the pathology laboratory.Virchows Archiv : an international journal of pathology · 2024Review
- The Puzzle of Preimplantation Kidney Biopsy Decision-Making Process: The Pathologist Perspective.Life (Basel, Switzerland) · 2024Review
- An Exploratory Review on the Potential of Artificial Intelligence for Early Detection of Acute Kidney Injury in Preterm Neonates.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence Advances in Transplant Pathology.Bioengineering (Basel, Switzerland) · 2023Review
- Histology for nephrology, from pre-implantation to post-transplant kidney biopsy. Lesson learned from ReBIrth (Renal BIopsy for Kidney Transplantation Therapy).Pathologica · 2023Review
- ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model.International journal of oral science · 2023Review
- Recurrent Membranoproliferative Glomerulonephritis After Kidney Transplantation: Risk Factors and Impact on Graft Survival.Annals of transplantation · 2023Article
- Horizon of the pre-implantation kidney biopsy for allocation: multidisciplinarity, methodology and innovation.Journal of nephrology · 2023Article
Corrections and comments
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Authors and funding
13 authors at 9 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy.
methodsA systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study.
resultsOf 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising.
conclusionAll studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice.
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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.