ArticleMedical physics2020
A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: Preliminary results.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 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
- 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
- 3D Adversarial Segmentation of Kidney-Transplant Across Multiple MRI Sequences Using Probabilistic and Anatomical Priors.Diagnostics (Basel, Switzerland) · 2026Article
- An applied study of blood oxygenation level-dependent and arterial spin labeling in early transplant renal function based on renal magnetic resonance angiography examination.Quantitative imaging in medicine and surgery · 2026Article
- Radiologic evaluation of the kidney transplant donor and recipient.Abdominal radiology (New York) · 2025Review
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- Artificial intelligence and machine learning for medical imaging: A technology review.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- A DEEP LEARNING-BASED CAD SYSTEM FOR RENAL ALLOGRAFT ASSESSMENT: DIFFUSION, BOLD, AND CLINICAL BIOMARKERS.Proceedings. International Conference on Image Processing · 2020Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 3 countries.
Funding
Abstract
purposeEarly assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function.
methodsThe developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm
resultsUsing a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88.
conclusionThe obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.
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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.