Nephrology

Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.

BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.

Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques.

 The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and subtyping on computed tomography (CT): clear cell RCC (ccRCC) versus non-ccRCC using two-dimensional (2D) neural network architecture and feature consistency modules.  This retrospective study included baseline CT scans from 196 histopathologically...

Self-improving generative foundation model for synthetic medical image generation and clinical applications.

In many clinical and research settings, the scarcity of high-quality medical imaging datasets has hampered the potential of artificial intelligence (A...

Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease.

BACKGROUND: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize ...

Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.

BACKGROUND: Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown t...

Machine learning in predicting heart failure survival: a review of current models and future prospects.

Heart failure is a complex and prevalent condition with significant implications for patient management and survival prediction. Traditional predictiv...

Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.

Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney rep...

Influence of renal function on blood pressure control and outcome in thrombolyzed patients after acute ischemic stroke: analysis of the ENCHANTED trial.

BACKGROUND: The effect of renal impairment in patients who receive intravenous thrombolysis for acute ischemic stroke (AIS) is unclear. We aimed to de...

Applicability of creatinine-based glomerular filtration rate assessment equations to patients with neurogenic bladder.

PURPOSE: Glomerular filtration rate (GFR) measured by dynamic renal scintigraphy (Gates method) is used in this study as the standard to investigate t...

Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging.

RATIONALE AND OBJECTIVES: The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk strat...

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung canc...

Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy,...

Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.

INTRODUCTION: Acute kidney injury (AKI) is notably prevalent after cardiac surgery for patients with active infective endocarditis. This study aims to...

Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients.

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transp...

Machine learning evaluation of a hypertension screening program in a university workforce over five years.

The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous o...

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.

Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various k...

Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients.

BACKGROUND: Heart failure is common in patients receiving hemodialysis. A high-flow arteriovenous fistula (AVF) may represent a modifiable risk factor...

CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.

This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN ...

How to incorporate generative artificial intelligence in nephrology fellowship education.

Traditional nephrology education faces challenges due to expanding medical knowledge, case complexity, and personalized learning needs. Generative art...

Reliability and quality of information provided by artificial intelligence chatbots on post-contrast acute kidney injury: an evaluation of diagnostic, preventive, and treatment guidance.

OBJECTIVE: The aim of this study was to evaluate the reliability and quality of information provided by artificial intelligence chatbots regarding the...