Urology

Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.

Artificial intelligence (AI) is rapidly transforming the landscape of chronic medical conditions, such as cardio-kidney-metabolic (CKM) issues linked to type 2 diabetes and obesity. It creates new opportunities to shift from reactive to proactive, data-driven care. Recent advances include predictive algorithms for hypoglycemia and hyperglycemia, decision-support tools for insulin titration, and ge...

A Cost-Efficient Multi-Angle Fusion Deep Learning for Ultrasound Localization Microscopy.

Ultrasound localization microscopy (ULM) enables super-resolution imaging of microvascular structures by localizing microbubbles from clutter-filtered ultrafast ultrasound data. However, conventional clutter filtering methods, particularly those based on singular value decomposition, are computationally intensive and thus impractical for real-time applications. In this study, we introduce AF-UNet,...

Prostate Targeting: Compact Robot With Harmonic Stepper Motors for MRI-Guided Needle Therapy.

The superior image quality and excellent contrast offered by Magnetic Resonance Imaging (MRI) make it an ideal tool for guiding interventional procedu...

Predicting Positive Surgical Margins in Robot-Assisted Prostatectomy Using Machine Learning Models.

OBJECTIVE: To develop a machine learning (ML) model predicting positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP). METH...

Enhancing urothelial carcinoma diagnosis with artificial intelligence-integrated urine cytology: Biopsy-validated accuracy and efficiency gain.

BACKGROUND: Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and tim...

Kidney health without borders: a multilingual kidney education platform.

Artificial Intelligence-Based Objective Evaluation of Nerve-Sparing Status Combined With Preserved Urethral Length Predicts Urinary Continence Recovery After Robot-Assisted Radical Prostatectomy.

OBJECTIVES: To evaluate nerve-sparing status objectively using an artificial intelligence method applied to intraoperative images and to determine whe...

Combining bioinformatics and machine learning to analyze and validate sepsis-related cell senescence genes and potential drugs.

Sepsis is a life-threatening organ malfunction induced by the host's abnormal reaction to infection. Sepsis can induce cellular senescence, thereby ex...

Frequency-Specific Alterations of Spontaneous Brain Activity in Stroke Patients with Upper Limb Motor Dysfunction: A Multi-Metric rs-fMRI and Machine Learning Study.

BACKGROUND: Post-stroke upper limb motor dysfunction is associated with complex alterations in brain function, but the frequency-specific characterist...

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 compli...

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 pelv...

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...

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...