Nephrology

Latest AI and machine learning research in nephrology for healthcare professionals.

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Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

BACKGROUND: Advanced machine learning methods combined with large sets of health screening data prov...

Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization.

To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisia...

Transplant nephrectomy with peritoneal window: Georgetown University experience.

OBJECTIVES: Transplant nephrectomy is a technically challenging procedure with high complication rat...

Metabolomics meets machine learning: Longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis.

This study aimed to investigate the differences in the metabolic profiles in serum of dairy cows tha...

Minimally invasive nephrectomy for inflammatory renal disease.

OBJECTIVE: Once chronic inflammatory renal disease (IRD) develops, it creates a severe peri-fibrotic...

Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method.

BACKGROUND: Kidney allocation is a multi-criteria and complex decision-making problem, which should ...

Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis o...

Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification.

Exosomes are endosome-derived vesicles enriched in body fluids such as urine, blood, and saliva. So ...

Ameliorative effect of virgin olive oil against nephrotoxicity following sub-chronic administration of ethephon in male rats.

BACKGROUND: Ethephon (EP) is the most famous plant growth regulator with different adverse effects o...

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

INTRODUCTION: Machine learning has been increasingly used to develop predictive models to diagnose d...

Pattern recognition and prognostic analysis of longitudinal blood pressure records in hemodialysis treatment based on a convolutional neural network.

OBJECTIVE: The aim of this study is to analyze and visualize blood pressure (BP) patterns during con...

Accelerating cardiovascular model building with convolutional neural networks.

The objective of this work is to reduce the user effort required for 2D segmentation when building p...

Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives.

Artificial intelligence is playing an increasingly important role in many fields of medicine, assist...

Prediction model development of late-onset preeclampsia using machine learning-based methods.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the ...

A non-linear mathematical model using optical sensor to predict heart decellularization efficacy.

One of the main problems of the decellularization technique is the subjectivity of the final evaluat...

Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams.

This paper presents a new approach for precisely estimating the renal vascular dominant region using...

A self-supervised strategy for fully automatic segmentation of renal dynamic contrast-enhanced magnetic resonance images.

PURPOSE: An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR...

Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor.

BACKGROUND: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care ...

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