Urology

Quantitative MRI of pediatric chronic kidney disease and renal fibrosis: potential non-invasive clinical biomarkers.

Chronic kidney disease (CKD) in children is a rare but serious condition characterized by progressive renal dysfunction and a range of systemic complications. Renal fibrosis plays a central role in disease progression, representing irreversible damage associated with poor outcomes. Currently, diagnosis and assessment of fibrosis rely primarily on invasive renal biopsy, which is limited by procedur...

Predicting De Novo Stress Urinary Incontinence After Colpocleisis Using Clinical and Pelvic Floor Ultrasound Parameters: Development and Internal Validation of a Multimodal Model.

INTRODUCTION AND HYPOTHESIS: De novo stress urinary incontinence (SUI) remains a clinically relevant complication after colpocleisis for advanced pelvic organ prolapse (POP), and reliable preoperative risk stratification remains limited. We aimed to develop and internally validate a multimodal prediction model incorporating clinical and pelvic floor ultrasound parameters to estimate the risk of de...

A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.

Accurate identification of renal cell types within tissue architecture is fundamental for understanding normal kidney physiology and detecting early p...

Explainable ensemble machine learning for predicting diabetes mellitus and identifying key risk factors: a population-based study in northern Bangladesh.

Diabetes mellitus (DM) is an escalating global public health concern, with a rapidly increasing burden in low- and middle-income countries, including ...

Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.

BACKGROUND: Patients with advanced chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR) < 45 ml/min/1.73 m²,...

Deep learning-based cervical cancer T-staging using MRI: multi-structure segmentation and classification.

AIMS: Cervical cancer has high incidence and mortality, seriously threatening women's survival and quality of life. Radiologists currently rely mainly...

Machine learning prediction of sudden cardiac death incorporating multiple lipid markers: evidence from the Taiwan Chin Shan community cohort.

BACKGROUND: Sudden cardiac death (SCD) is a major contributor to cardiovascular mortality, but reliable long-term risk prediction in community-based p...

What's next for VI-RADS? Updates and future perspectives from the ACR VI-RADS steering committee.

Since the introduction of the Vesical Imaging-Reporting and Data System (VI-RADS), MRI has become an important imaging modality in the management of p...

Repurposing cardiovascular disease prediction models for cancer.

INTRODUCTION: Population cancer screening detects the presence of early-stage disease rather than assessing future disease risk. We evaluated whether ...

Development and External Validation of a Machine Learning-Based Risk Classification Score for Prostate Cancer-Specific Cardiovascular Risk.

PURPOSE: Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and...

Deep learning-based prediction of trace silver concentrations in marine sediments from mid-infrared spectroscopy.

Silver (Ag) is a biologically toxic element, and the rapid detection of its concentrations in environmental samples is thus of great importance. This ...

AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.

BACKGROUND AND OBJECTIVE: Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible...

Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer.

Non-muscle-invasive bladder cancer (NMIBC) has high rates of recurrence and progression, yet its molecular landscape across different ethnicities rema...

Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis.

Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diag...

MSTM-Net: a two-stage prostate cancer segmentation network based on swin-transformer-mamba architecture.

BACKGROUND: Magnetic resonance imaging (MRI) has become a core imaging modality for prostate cancer screening and diagnosis. Accurate and automatic se...

Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer.

OBJECTIVES: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigat...

PGFD-YOLO: A dual-modal object detection framework with progressive gated fusion and foreground-guided distillation.

Effective waste classification is essential for improving resource recovery and reducing landfill burden. However, existing methods predominantly rely...

Chronic kidney disease of unknown causes in Mesoamerica. An approach from the Perspective of Medical Geology.

In Mesoamerica, new cases of chronic kidney disease of unknown cause (CKDu) are increasing each year, including those requiring renal replacement ther...

Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.

Making the decision between technically challenging partial nephrectomy (PN) and radical nephrectomy (RN) in patients with complex renal cell carcinom...

Capsid Engineering of Adeno-Associated Viruses for Targeted Gene Therapy in Kidney Diseases.

The global burden of chronic and genetic kidney diseases poses a significant challenge to healthcare systems. Current therapies, including dialysis, t...