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Kidney Transplantation

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Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

Journal of the American Society of Nephrology : JASN
BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with perio...

Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models.

International journal of medical informatics
INTRODUCTION: Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of the kidney transplant population makes predicting graft outcomes extremely challenging. Several kidn...

[Renal graft survival in patients transplanted from organs of deceased donors].

Revista medica del Instituto Mexicano del Seguro Social
BACKGROUND: In Mexico, out of the total number of transplants it was reported, in 2014, a frequency of 29% of deceased donor renal transplantation (DDRT). The use of kidneys from deceased elderly donors is increasing over the years. Currently, some a...

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Microscope Diagnostic System (MMDx). The present study was designed to optimize the accuracy and stabilit...

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides.

Scientific reports
Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of ren...

Using machine learning and an ensemble of methods to predict kidney transplant survival.

PloS one
We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell's concordance index, than the Esti...

Robot-assisted laparoscopic vs laparoscopic donor nephrectomy in renal transplantation: A meta-analysis.

Clinical transplantation
BACKGROUND: Currently, the robot-assisted laparoscopic donor nephrectomy (RDN) technique is used for live donor nephrectomy. Does it provide sufficient safety and benefits for living donors? We conducted a meta-analysis to assess the safety and effic...

Renal Function Impairment in Kidney Transplantation: Importance of Early BK Virus Detection.

Transplantation proceedings
BACKGROUND: BK virus allograft nephropathy is a major complication of kidney transplantation that markedly reduces graft survival (50% graft failure 24 months after being diagnosed). BK virus replication can occur at any time posttransplantation. Vir...