AIMC Topic: Allografts
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Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking.
Renal failure
Dec 4, 2024
Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) ...
Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.
Frontiers in immunology
Jul 5, 2024
BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning sys...
Predicting kidney allograft survival with explainable machine learning.
Transplant immunology
May 24, 2024
INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning meth...
Novel non-invasive method for urine mapping: Deep-learning-enabled SERS spectroscopy for the rapid differential detection of kidney allograft injury.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Apr 4, 2024
The kidney allograft has been under continuous attack from diverse injuries since the very beginning of organ procurement, leading to a gradual decline in function, chronic fibrosis, and allograft loss. It is vital to routinely and precisely monitor ...
Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.
Nature medicine
Mar 21, 2022
Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads t...
Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study.
The Lancet. Digital health
Oct 28, 2021
BACKGROUND: Kidney allograft failure is a common cause of end-stage renal disease. We aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for kidney transplant recipients by generating continuously refined predi...
Artificial Intelligence
Kidney
Kidney Transplantation
Humans
Models, Biological
Middle Aged
Glomerular Filtration Rate
Prognosis
Female
Renal Insufficiency
Area Under Curve
Proteinuria
Risk Assessment
Reproducibility of Results
Bayes Theorem
Transplant Recipients
Postoperative Complications
Adult
Allografts
Male
Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.
Histopathology
Mar 8, 2021
Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be ...
Early Projection of Kidney Allograft Rejection Through Deep Learning: A Way Forward.
Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation
Sep 17, 2020
Machine learning for predicting long-term kidney allograft survival: a scoping review.
Irish journal of medical science
Aug 6, 2020
Supervised machine learning (ML) is a class of algorithms that "learn" from existing input-output pairs, which is gaining popularity in pattern recognition for classification and prediction problems. In this scoping review, we examined the use of sup...
Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Mar 26, 2020
A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to ass...
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