ArticleFrontiers in medicine2021
Deep Learning Algorithms for the Prediction of Posttransplant Renal Function in Deceased-Donor Kidney Recipients: A Preliminary Study Based on Pretransplant Biopsy.
Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Time-Zero Kidney Biopsy and Deceased-Donor Transplant Outcomes: A Systematic Review and Meta-Analysis of Prognostic Value, Prediction, and Clinical Utility.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Prognostic significance of early cortical functional integrity measured byEuropean journal of nuclear medicine and molecular imaging · 2026Article
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.Journal of the American Society of Nephrology : JASN · 2025Article
- European Society for Organ Transplantation (ESOT)-TLJ 3.0 Consensus on Histopathological Analysis of Pre-Implantation Donor Kidney Biopsy: Redefining the Role in the Process of Graft Assessment.Transplant international : official journal of the European Society for Organ Transplantation · 2023Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPosttransplant renal function is critically important for kidney transplant recipients. Accurate prediction of graft function would greatly help in deciding acceptance or discard of allocated kidneys.
methods
resultsIn total, 219 recipients with H&E-stained slides of the donor kidneys were included for analysis [biopsies from standard criteria donor (SCD)/expanded criteria donor (ECD) was 191/28]. The results showed distinct improvements in the prediction performance of the deep learning algorithm plus the clinical characteristics model. The EfficientNet-B5 plus clinical data model showed the lowest mean absolute error (MAE) and root mean square error (RMSE). Compared with the clinical data model, the area under the receiver operating characteristic (ROC) curve (AUC) of the clinical data plus image model for eGFR classification increased from 0.69 to 0.83. In addition, the predictive performance for RGF increased from 0.66 to 0.80. Gradient-weighted class activation mappings (Grad-CAMs) showed that the models localized the areas of the tubules and interstitium near the glomeruli, which were discriminative features for RGF.
conclusionOur results preliminarily show that deep learning for formalin-fixed paraffin-embedded H&E-stained WSIs improves graft function prediction accuracy for deceased-donor kidney transplant recipients.
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Registered trials
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