Nephrology

Investigation on explainable machine learning models to predict chronic kidney diseases.

Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world's population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflict...

Single-Port Multiport Robot-Assisted Partial Nephrectomy: A Meta-Analysis.

Several centers have reported their experience with single-port robot-assisted partial nephrectomy (SP-RAPN); however, it is uncertain if utilization of this platform represents an improvement in outcomes compared to multiport robot-assisted partial nephrectomy (MP-RAPN). To evaluate this, we performed a meta-analysis to compare the perioperative, oncological, and functional outcomes between SP-R...

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions.

BACKGROUND: The growing complexity of patient data and the intricate relationship between heart failure (HF) and acute kidney injury (AKI) underscore ...

DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients.

BACKGROUND: Chronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) qualit...

Effect of Febuxostat versus Allopurinol on the Glomerular Filtration Rate and Hyperuricemia in Patients with Chronic Kidney Disease.

Hyperuricemia is a risk factor for the progression of chronic kidney disease (CKD). We compared febuxostat versus allopurinol in the progression of CK...

Acute Kidney Injury in Acute Myocardial Infarction and Its Outcome at 3 and 6 Months.

Epidemiological data on the prevalence of acute kidney injury (AKI) in acute coronary syndrome are sparse, with most studies having been conducted ret...

Study of Serum Fibroblast Growth Factor 23 as a Predictor of Endothelial Dysfunction among Egyptian Patients with Diabetic Kidney Disease.

Endothelial dysfunction in patients with diabetic nephropathy is caused by nontraditional factors in addition to common risk factors (e.g., hypertensi...

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.

Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation ...

The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.

We investigate risk factors for severe COVID-19 in persons living with HIV (PWH), including among racialized PWH, using the U.S. population-sampled Na...

Donor hepatitis C status is not associated with an increased risk of acute rejection in kidney transplantation.

INTRODUCTION: In renal transplantation, donor hepatitis C virus (HCV) status is crucial to consider when selecting a recipient given the high likeliho...

Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chro...

Interactive content-based image retrieval with deep learning for CT abdominal organ recognition.

Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automa...

Attention-Based Deep Learning Model for Prediction of Major Adverse Cardiovascular Events in Peritoneal Dialysis Patients.

Major adverse cardiovascular events (MACE) encompass pivotal cardiovascular outcomes such as myocardial infarction, unstable angina, and cardiovascula...

[Advances in computational quantitative nephropathology].

BACKGROUND: Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on ge...

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Use of Temporally Validated Machine Learning Models To Predict Outcomes of Percutaneous Nephrolithotomy Using Data from the British Association of Urological Surgeons Percutaneous Nephrolithotomy Audit.

BACKGROUND AND OBJECTIVE: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes...

Viral load kinetics and the clinical consequences of cytomegalovirus in kidney transplantation.

BACKGROUND: Despite advances in clinical management, cytomegalovirus (CMV) infection remains a serious complication and an important cause of morbidit...

Isolation and characterization of exosome-enriched urinary extracellular vesicles from Dent's disease type 1 Spanish patients.

BACKGROUND AND OBJECTIVES: Dent's disease type 1 (DD1) is a rare X-linked hereditary pathology caused by CLCN5 mutations that is characterized mainly ...

Artificial intelligence and machine learning trends in kidney care.

BACKGROUND: The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This...

Noninvasive Assessment of Kidney Injury by Combining Structure and Function Using Artificial Intelligence-Based Manganese-Enhanced Magnetic Resonance Imaging.

Contrast-enhanced magnetic resonance imaging (MRI) is seriously limited in kidney injury detection due to the nephrotoxicity of clinically used gadoli...