ArticleScientific reports2019
A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Multiparametric functional MRI for detection of early renal allograft dysfunction after kidney transplantation: a systematic review and meta-analysis.BMC nephrology · 2026Pooled it
- Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI.Magma (New York, N.Y.) · 2020Pooled it
- Advances in HLA and Non-HLA Immune Profiling: Integrating Next-Generation Sequencing and Artificial Intelligence in Transplantation.Annals of laboratory medicine · 2026Review
- Diagnostic accuracy of MRI and US for identifying acute rejection after allogeneic kidney transplantation: a systematic review and meta-analysis.Clinics (Sao Paulo, Brazil) · 2026Article
- Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction.Abdominal radiology (New York) · 2026Article
- Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease.International journal of molecular sciences · 2026Review
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.Briefings in bioinformatics · 2024Review
- A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation.Communications medicine · 2023Article
- A two-stage renal disease classification based on transfer learning with hyperparameters optimization.Frontiers in medicine · 2023Article
- Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review.Frontiers in surgery · 2023Review
- Potential Utility of Neutrophil-to-Lymphocyte, Platelet-to-Lymphocyte, and Neutrophil, Lymphocyte, and Platelet Ratios in Differential Diagnosis of Kidney Transplant Acute Rejection: A Retrospective, Propensity Score Matched Analysis.Annals of transplantation · 2022Article
- Artificial Intelligence You Can Trust: What Matters Beyond Performance When Applying Artificial Intelligence to Renal Histopathology?Journal of the American Society of Nephrology : JASN · 2022Review
- Review
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature.Journal of clinical medicine · 2021Review
- Artificial Intelligence-related Literature in Transplantation: A Practical Guide.Transplantation · 2021Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
Abstract
This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.
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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.