Nephrology

SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning

Accurate segmentation of tubular and curvilinear structures, such as blood vessels, neurons, and road networks, is crucial in various applications. A key challenge is ensuring topological correctness while maintaining computational efficiency. Existing approaches often employ topological loss functions based on persistent homology, such as Betti error, to enforce structural consistency. However,...

Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records

Access to real-world healthcare data is limited by stringent privacy regulations and data imbalances, hindering advancements in research and clinical applications. Synthetic data presents a promising solution, yet existing methods often fail to ensure the realism, utility, and calibration essential for robust survival analysis. Here, we introduce Masked Clinical Modelling (MCM), an attention-bas...

Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation

Accurate segmentation of thin, tubular structures (e.g., blood vessels) is challenging for deep neural networks. These networks classify individual ...

Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model

This study presents a 3D flow-matching model designed to predict the progression of the frozen region (iceball) during kidney cryoablation. Precise ...

KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease

Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clini...

Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagn...

AI-Augmented Kidney Stone Composition Analysis with Auto-Release Improves Quality, Efficiency, Cost-Effectiveness, and Staff Satisfaction.

BACKGROUND: We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented ki...

Estimating Blood Pressure with a Camera: An Exploratory Study of Ambulatory Patients with Cardiovascular Disease

Hypertension is a leading cause of morbidity and mortality worldwide. The ability to diagnose and treat hypertension in the ambulatory population is...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Incident Hypertension.

IMPORTANCE: Hypertension underpins significant global morbidity and mortality. Early lifestyle intervention and treatment are effective in reducing ad...

Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term ...

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.

BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are...

A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.

Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Immunodeficiency Virus-infected (HIV-infected) patie...

Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee.

In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The ass...

3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer

Purpose: This study aims to develop and validate a method for synthesizing 3D nephrographic phase images in CT urography (CTU) examinations using a ...

Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database

Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models us...

Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts

We introduce a novel contextual embedding model med-gte-hybrid that was derived from the gte-large sentence transformer to extract information from ...

AutoMedPrompt: A New Framework for Optimizing LLM Medical Prompts Using Textual Gradients

Large language models (LLMs) have demonstrated increasingly sophisticated performance in medical and other fields of knowledge. Traditional methods ...

The ETKidney simulator: a discrete event simulator to assess the impact of alternative kidney allocation rules in Eurotransplant

Over 10,000 candidates wait for a kidney transplantation in Eurotransplant, and are prioritized for transplantation based on the allocation rules of...

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR 
Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung can...

LIDDIA: Language-based Intelligent Drug Discovery Agent

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space ...