Nephrology

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and mortality. Machine learning (ML), combined with parameters such as shock indices, can potentially improve CIN risk prediction by analyzing complex variable interactions and creating accessible, clinically applicable models. Methods: This retrospective...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular drivers remain poorly defined. Dysregulated coagulation is increasingly implicated in SA-AKI pathogenesis through microvascular thrombosis and immune crosstalk, but kidney-specific coagulation biomarkers remain uncharacterized. Methods: Using murine (M...

Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning.

OBJECTIVES: Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, which causes severe complications and symptoms for rec...

Quantitative CT biomarkers for renal cell carcinoma subtype differentiation: a comparison of DECT, PCT, and CT texture analysis.

OBJECTIVE: To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusion CT (PCT), and dual-energy CT (DECT) in distingu...

Liver Fat Fraction and Machine Learning Improve Steatohepatitis Diagnosis in Liver Transplant Patients.

Machine learning identifies liver fat fraction (FF) measured by H MR spectroscopy, insulinemia, and elastography as robust, non-invasive biomarkers fo...

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communi...

Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.

INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic ...

Molecule-Responsive SERS Sensors for Urine Diagnosis of Kidney Diseases Enhanced by Neural Networks.

Early diagnosis of kidney disease is crucial for treatment and prognosis. Compared with kidney biopsy, a noninvasive urine-based diagnosis method of k...

Integrative analyses of 16S rDNA sequencing and serum metabolomics demonstrate significant roles for the oral microbiota and serum metabolites in post-kidney transplant diabetes mellitus.

Oral microbiota and serum metabolites play crucial roles in diabetes, but their relationship with post-transplant diabetes mellitus (PTDM), a common c...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposu...

Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies.

BACKGROUND: Assessing the extent of inflammation in peritubular capillaries (PTCs) is important for diagnosing antibody-mediated rejection in kidney t...

An informed machine learning based environmental risk score for hypertension in European adults.

BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle wit...

Deep Learning-Based Semantic Segmentation for Real-Time Kidney Imaging and Measurements with Augmented Reality-Assisted Ultrasound

Ultrasound (US) is widely accessible and radiation-free but has a steep learning curve due to its dynamic nature and non-standard imaging planes. Ad...

A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation

Generating radiology reports from CT scans remains a complex task due to the nuanced nature of medical imaging and the variability in clinical docum...

Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantati...

Label-free urinary protein detection through machine learning analysis of single droplet evaporation patterns.

BACKGROUND: Chronic kidney disease (CKD) is a major global public health issue, with a steadily increasing incidence. Urinary protein detection serves...

Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures

Accurate multi-class tubular modeling is critical for precise lesion localization and optimal treatment planning. Deep learning methods enable autom...

Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency

Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, which introduces variability in diagnosis. This study invest...

A Spatial Metabolomics Annotation Workflow Leveraging Cyclic Ion Mobility and Machine Learning-Predicted Collision Cross Sections.

In nontargeted spatial metabolomics, accurate annotation is crucial for understanding metabolites' biological roles and spatial patterns. MS mass spec...

HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification

Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not ...