ArticleNature communications2024
A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06505200 (An Integrative Clinicopathological Decision Support System for Kidney Transplant Rejection), which is not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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.
An Integrative Clinicopathological Decision Support System for Kidney Transplant Rejection
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Preimplantation Histopathologic Assessment of Deceased Donor Kidneys: Recommendations from the Banff Time-Zero Biopsy Working Group.Journal of the American Society of Nephrology : JASN · 2025Guideline
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Editorial: Methods in alloimmunity and transplantation: 2025.Frontiers in immunology · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- Kidney and Bladder Transplantation: Advances, Barriers, and Emerging Solutions.Medicina (Kaunas, Lithuania) · 2025Review
- Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.Current opinion in organ transplantation · 2025Review
- Development and Validation in Porcine and Human Models of a Bioimpedance Spectroscopy System for the Objective Assessment of Kidney Graft Viability.Sensors (Basel, Switzerland) · 2025Article
- Machine learning-based model for predicting all-cause mortality in severe pneumonia.BMJ open respiratory research · 2025Article
- Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.BMC nephrology · 2025Article
- Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Angiotensin II type 1 receptor antibodies as a contributor to microvascular inflammation in kidney transplant recipients: insights from statistical and artificial intelligence based approaches.Frontiers in immunology · 2025Article
- AI Thinking: a framework for rethinking artificial intelligence in practice.Royal Society open science · 2025Article
- A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver.Cell reports. Medicine · 2024Article
- Machine learning-driven virtual biopsy system may increase organ discards at aggressive kidney transplant centers.Nature communications · 2024Article
- Reply to: Machine learning-driven virtual biopsy system may increase organ discards at aggressive kidney transplant centers.Nature communications · 2024Article
- Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.Science advances · 2024Article
Corrections and comments
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Authors and funding
39 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.
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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.