Urology

Integrating multi-omics and machine learning to decipher the molecular pathways of bisphenol a-associated lactylation-related genes driving bladder cancer.

In this study, we systematically investigated bladder cancer-related gene signatures using a toxicogenomics-informed framework, with particular attention to genes associated with lactylation-related pathways. Multi-omics data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were integrated, and Weighted Gene Co-expression Network Analysis (WGCNA), a toxicology database, an...

The role of AI in prostate cancer care: Assessing the role of chatbots versus urologists in patient communication and empathy.

OBJECTIVE: To date, the integration of artificial intelligence (AI) in healthcare has expanded rapidly, offering new tools for patient education and communication. In prostate cancer (PCa), where information needs are high and emotionally sensitive, AI-driven chatbots (CB) may enhance patient engagement. This study aims to compare the performance and perceived quality of responses from CB versus u...

Machine learning predicts postoperative mortality in a Latin American cardiovascular surgery multicenter cohort: Toward precision risk stratification.

OBJECTIVE: A total of 28% of global cardiac surgeries are performed in Latin America; however, surgeons there are faced with many preventable deaths, ...

Supervised Learning for Collagen Segmentation in Bright-Field Histology: A Comparative Evaluation of U-Net and MLP.

PURPOSE: Quantifying collagen in histological slides is essential for diagnosing and monitoring fibrosis. However, the combination of PicroSirius Red ...

Expert Evaluation of the Perceived Accuracy, Relevance, and Safety of Large Language Model-Generated Patient Information in Geriatrics: Cross-Condition Study.

BACKGROUND: Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must ...

Multimodal deep learning model for multiclass classification of renal tumors.

Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and vali...

Explainable and visualizable machine learning model development and validation for 5-year postoperative survival prediction in prostate cancer patients aged ≥ 65 years.

BACKGROUND: Machine Learning (ML) models have achieved outstanding performance in predicting post-surgical survival. However, the "black-box" nature o...

Automated counting of prostate cell types with image processing and machine learning.

Traditional cell counting in clinical and research settings often relies on hemocytometry, a manual technique that is labor-intensive and prone to hum...

Identification of potential biomarkers for osteoporosis and chronic kidney disease through bioinformatics and machine learning algorithm.

OBJECTIVE: The purpose of this study is to identify hub genes associated with both osteoporosis (OP) and chronic kidney disease (CKD) through bioinfor...

Detection of benign prostatic hyperplasia using RGB prostate images and deep learning.

This study investigates the identification of Benign Prostatic Hyperplasia (BPH) through a deep learning-based analysis of RGB prostate histopathologi...

Artificial Intelligence in Urologic Documentation: A Review of Emerging Capabilities and the Ongoing Need for Human Oversight.

PURPOSE OF REVIEW: Clinical documentation continues to expand in volume and complexity, spanning outpatient encounters, inpatient summaries, patient-p...

Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination.

This study examines the influence of divisor selection on the efficacy of advanced analytical spectrophotometric methods that integrate artificial int...

Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.

Purpose To simulate an artificial intelligence (AI)-driven triaging workflow in which an AI system, using high-confidence thresholds, assesses a subse...

Investigation of the Accuracy and Contributing Factors of AI-Based Diagnosis of Urothelial Carcinoma in Canine Abdominal Radiography.

Urothelial carcinoma (UC) is a highly malignant urinary cancer of the transitional epithelium in dogs. Recent advances in artificial intelligence (AI)...

Deep learning "super resolution" versus iterative reconstruction: Phantom-based image quality assessment.

BACKGROUND: Deep learning reconstruction (DLR) algorithms have begun replacing iterative reconstruction (IR) in CT. Besides the potential to reduce no...

Classifying Kidney Tumor via Adaptive SE-ResNeXt With Mutli-Novel Loss Function and Renal Mass Segmentation Using Pyramidal Attention-Based R2Unet++ Model.

Kidney tumors are one of the prevalent types of tumor globally and it has become a significant health concern. It is the most frequent type of urologi...

Combined Effects of Long-Term Sulfur Dioxide Exposure and Environmental Heat Stress on the Risk of Kidney Stone Disease.

OBJECTIVES: Kidney stone disease is influenced by environmental, metabolic, and climatic factors. Although sulfur dioxide and environmental heat stres...

Automated detection of mulberry bodies in urinary sediment for non-invasive Fabry disease screening.

CT-based body composition and its change through time in relation to outcomes in participants screened for lung cancer.

BACKGROUND: Computed Tomography (CT) scans allow opportunistic evaluation of body composition. We investigated whether body composition and change thr...

Multi-scale evidence chain: Linking environmental BPA exposure to ovarian cancer through integrated omics and experimental models.

BACKGROUND: Ovarian cancer (OC) is a leading cause of female cancer mortality. Beyond genetic and reproductive risk factors, emerging evidence suggest...