Nephrology

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

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Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning.

BACKGROUND: Allogeneic hematopoietic stem transplantation (allo-HSCT) constitutes a curative treatme...

Using deep learning to differentiate among histology renal tumor types in computed tomography scans.

BACKGROUND: This study employed a convolutional neural network (CNN) to analyze computed tomography ...

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning.

Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary...

Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

Stroke is a significant health concern in China. Differences in stroke risk between rural and urban ...

Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.

BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant su...

Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.

BACKGROUND: The principles of urgency, utility, and benefit are fundamental concepts guiding the eth...

Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis.

This study investigates the role of basement membrane-related genes in kidney fibrosis, a significan...

Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.

Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been...

Measuring kidney stone volume - practical considerations and current evidence from the EAU endourology section.

PURPOSE OF REVIEW: This narrative review provides an overview of the use, differences, and clinical ...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

This study aims to investigate the association between visit-to-visit blood pressure variability (VV...

Navigating advanced renal cell carcinoma in the era of artificial intelligence.

BACKGROUND: Research has helped to better understand renal cell carcinoma and enhance management of ...

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) an...

A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.

Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capab...

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited ...

Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.

BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. Th...

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at signif...

CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.

BACKGROUND: While the CT-based method of detecting clinically significant portal hypertension (CSPH)...

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