Nephrology

Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.

OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progression. This study developed a machine learning (ML) model to predict membership in a high-risk longitudinal estimated glomerular filtration rate (eGFR) trajectory associated with subsequent kidney replacement therapy (KRT) initiation. METHODS: We condu...

Evaluating Iterative Deep Learning as a Labeling-Efficient Strategy for Tubular Segmentation in Digital Nephropathology.

Chronic kidney disease (CKD) is a prevalent condition worldwide and a significant global health burden that is expected to increase in the coming decades. Morphological evaluation of renal tubules is critical for diagnosis and prognosis; however, manual annotation is labor-intensive and time-consuming. Automatic AI-based segmentation offers a promising solution, yet it still depends on extensive m...

Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.

BACKGROUND: Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models. OBJECTIVE: We d...

Genome-wide analysis of the invasive fungal pathogen Neopestalotiopsis reveals high genomic diversity, effector repertoires, and the assessment of fungicide resistance risks.

Neopestalotiopsis species (spp.) have recently emerged as major pathogens worldwide, causing leaf spot, fruit rot, and crown rot of strawberry, result...

Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.

The Banff Classification, established in 1991, provides a global standard for diagnosing and grading kidney transplant pathology, evolving through reg...

Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients.

BACKGROUND: Spontaneous bacterial peritonitis (SBP) remains a life-threatening complication of liver cirrhosis, requiring accurate and rapid predictio...

Epigenetic-Metabolic interplay in chronic kidney disease mortality: insights from grimage acceleration and transcriptomic profiling.

BACKGROUND: Chronic kidney disease (CKD) is associated with a substantially elevated risk of mortality. Although GrimAge acceleration (GAA) and metabo...

Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces.

Machine learning (ML) establishes a new paradigm for electrocatalyst and electrolyte research by coupling high-throughput screening (HTS) with a data-...

Elucidating the pathogenic mechanism underlying plasticizer-induced chronic kidney disease via integrated network toxicology and machine learning.

Plasticizers are ubiquitous plastic additives that have been linked to multiple adverse health outcomes and elevated risks of chronic kidney disease (...

Overdiagnosis in renal cancer: the hidden consequence of modern imaging.

The global burden of renal cell carcinoma (RCC) has risen substantially over the past three decades, while mortality rates have remained largely stabl...

Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning.

Acute kidney injury (AKI) is a severe and frequent complication following bee stings, with a reported incidence of 30-50%. Early identification is cli...

Employing network toxicology, molecular docking, machine learning, and single-cell analysis to analyze BPA exposure-induced ccRCC.

This study investigates the molecular mechanisms underlying bisphenol A (BPA)-induced clear cell renal cell carcinoma (ccRCC). We integrated transcrip...

Mechanism-Driven Self-Powered Biosensing: Integrating Entropy-Controlled Nanocatalysis with Machine Learning on a Generalizable Hydrogel Platform.

Conventional self-powered biosensors often face a trade-off between sensitivity and operational stability, largely hindered by the intrinsic instabili...

Emerging applications of traditional and generative artificial intelligence in Liver Transplantation.

Artificial intelligence (AI) has emerged as a transformative force in liver transplantation (LT), spanning patient selection, donor-recipient matching...

Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study.

BACKGROUND: Protein-energy wasting (PEW) is common in chronic kidney disease (CKD) and is linked to poor outcomes. Early risk stratification may enabl...

Echocardiography-Based Machine Learning Model for Atrial Fibrillation Risk Assessment in Hypertension.

BACKGROUND: Early identification of atrial fibrillation (AF) allows for timely interventions to reduce cardiovascular complications. Risk scores inclu...

Risk Prediction of Early-Onset Hepatocellular Carcinoma: Derivation and Validation in a Nationwide Young Adult Cohort.

BACKGROUND: The global incidence of early-onset hepatocellular carcinoma (eHCC) is increasing significantly; however, specific risk prediction tools f...

Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.

PURPOSE: Our purpose was to develop and validate an integrated clinical deep learning radiomics (DLR) nomogram for differentiation of fat-poor angiomy...

Comparison of human vs AI-generated feedback: results from an online survey study in the GlomCon glomerular disease fellowship program.

Optimizing Renal Fibrosis Assessment in Patients with Chronic Kidney Disease: Feature and Decision-Level Fusion in Machine Learning Using Clinical and Ultrasound Information.

PURPOSE: Machine learning has been extensively applied in nephrology. This study aims to evaluate and compare the effectiveness of various information...