Nephrology

Graph neural networks for computational nephrology.

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.

BACKGROUND: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence-enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD wo...

Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions.

Artificial intelligence (AI) and machine learning (ML) applications have emerged as transformative technologies in nephrology, particularly in dialysi...

Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis.

Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification re...

Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice.

Modeling electrocatalytic reactions at solid-liquid interfaces requires capturing both the quantum-mechanical processes at the electrode surface and t...

Proactive haemodynamic management using the hypotension prediction index during caesarean section: a randomised controlled study.

INTRODUCTION: Intra-operative hypotension is common during caesarean section and may result in adverse maternal effects, such as nausea and vomiting. ...

Artificial intelligence-based prediction of postoperative sepsis in patients undergoing suction mini-percutaneous nephrolithotomy in real world practice: a multicenter analysis from the global Suction Lechnology Utility in Mini-PCNL Study (STUMPS) registry.

The understandability and actionability of AI-generated information for patients with chronic kidney disease.

OBJECTIVES: This study assessed the understandability, actionability, and overall quality of Artificial Intelligence (AI)-generated responses to frequ...

The emerging role of machine learning-based methods in cancer classification using microRNA.

Early detection and accurate classification of cancer are crucial to improving patient outcomes. Diagnosis and classification of tumors using conventi...

Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches.

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dys...

Molecular Stratification of Antiphospholipid Syndrome Through Integrative Analysis of the Whole-Blood RNA Transcriptome.

OBJECTIVE: Antiphospholipid syndrome (APS) is a thromboinflammatory disorder characterized by clinical and mechanistic heterogeneity that complicates ...

Construction of a deep learning-based predictive model for delayed graft function in kidney transplantation.

BACKGROUND: Delayed graft function (DGF) is a major complication of kidney transplantation that adversely affects long-term graft survival. This study...

Long-Term Follow-Up in Liver Transplantation and Combined Cardiac Surgery: Validation and Refinement of the LT+CS Risk Score Using Advanced Machine Learning.

BACKGROUND: Combined liver transplantation (LT) and cardiac surgery (LT+CS) is a therapeutic option for patients with end-stage liver disease (ESLD) a...

Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.

Diabetic nephropathy (DN) represents the predominant microvascular complication associated with diabetes mellitus; however, existing diagnostic techni...

Early Risk Factor Prediction in Chronic Kidney Disease Diagnosis Using Feature Selection and Machine Learning Algorithms.

Chronic kidney disease, CKD in short, is a kind of long-term kidney illness in which rapid deterioration of kidney function is observed over a period ...

Integrative transcriptomic and machine learning framework reveals candidate genes and potential mechanisms of aflatoxin B1 exposure in breast cancer.

Aflatoxin B1 (AFB1), a known mycotoxin and environmental hazard, has been linked to breast cancer, yet the exact biological pathways remain poorly cha...

Predicting vesicoureteral reflux outcomes using artificial intelligence: A critical appraisal using APPRAISE-AI.

Vesicoureteral reflux (VUR) is a common congenital urinary tract anomaly in children, associated with recurrent urinary tract infections (UTIs) and lo...

Renal Clinical Study Participants Support Data Sharing and Use of Artificial Intelligence.

INTRODUCTION: Kidney diseases are a global health concern. Sharing data from renal clinical studies and the use of artificial intelligence (AI) can ad...

A deep learning and morphometric hybrid model for automated quantification of kidney interstitial fibrosis in trichrome-stained whole-slide image.

BACKGROUND: Interstitial fibrosis (IF) is the strongest predictor of chronic kidney disease progression. Visual estimation of IF from trichrome (TRI)-...

Research on the Application Value of Blood Plasma Autofluorescence Spectroscopy Model Constructed by Machine Learning Algorithms in Identifying the Types of Post-operative Kidney Transplantation Injury.

BACKGROUND: This study aimed to assess the value of blood plasma autofluorescence spectroscopy in classifying renal injury after renal transplantation...