Nephrology

Application of Artificial Intelligence in Clinical Practice - Perception of a Multinational Group of Nephrologists.

This study investigates the perception of a multinational group of nephrologists on artificial intelligence (AI) application in clinical practice. A validated on-line survey was performed in March 2024, in 4 continents. The results revealed a prevalent familiarity with AI and machine learning (ML) terms, but traditional tools remained favored for clinical decision support. AI's future relevance wa...

Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement.

Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize resource allocation. The study aimed to develop a model for early prediction of Renal Replacement Therapy (RRT) requirement within 24 hours of ICU admission, utilizing machine learning techniques and SHapley Additive exPlanations (SHAP). It assessed va...

Exploring Offline Large Language Models for Clinical Information Extraction: A Study of Renal Histopathological Reports of Lupus Nephritis Patients.

Open source, lightweight and offline generative large language models (LLMs) hold promise for clinical information extraction due to their suitability...

Bridging Gaps with Generative AI: Enhancing Hypertension Monitoring Through Patient and Provider Insights.

This study introduces a Generative Artificial Intelligence (GenAI) assistant designed to address key challenges in Remote Patient Monitoring (RPM) for...

Personalized graph feature-based multi-omics data integration for cancer subtype identification

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment ch...

A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.

The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of...

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC...

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensi...

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Hist...

Using ChatGPT for Kidney Transplantation: Perceived Information Quality by Race and Education Levels.

BACKGROUND: Kidney transplantation is a complex process requiring extensive preparation and ongoing monitoring. Artificial intelligence (AI)-powered c...

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in la...

Towards early detection of chronic kidney disease based on gait patterns: IMU-based approach using neural networks.

The aging population has led to an increased prevalence of chronic kidney disease (CKD), associated with a higher incidence of gait disturbances and r...

Ensemble Learning Approaches for Automatic Detection of Chronic Kidney Disease Stages during Sleep.

This study investigates the use of ensemble learning methods for the automatic detection of chronic kidney disease (CKD) stages during sleep. We appli...

LLM-based kidney disease diagnostic framework for Pathologists.

Large language models revolutionize the recent paradigm in the medical field and its contributing to various applications, diversified from clinical d...

Predicting Donor Selection and Multi-Organ Transplantation within Organ Procurement Organizations Using Machine Learning.

Organ procurement organizations (OPOs) play a crucial role in the field of organ transplantation, serving as key intermediaries in the process of orga...

Selective Clamping for Robot-Assisted Surgical Procedures.

Partial nephrectomy, the gold standard treatment for renal tumors, is performed with clamping of the renal arteries, in order to interrupt the blood f...

3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.

The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnet...

Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning.

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndr...

[Artificial intelligence research advances in discrimination and diagnosis of pulmonary ground-glass nodules].

Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates f...

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

BACKGROUND: Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chron...