Urology

Development and Validation of Ultrasound Hemodynamic-based Prediction Models for Acute Kidney Injury After Renal Transplantation.

RATIONALE AND OBJECTIVES: Acute kidney injury (AKI) post-renal transplantation often has a poor prognosis. This study aimed to identify patients with elevated risks of AKI after kidney transplantation.

Predicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005-2020.

Overactive bladder (OAB), a prevalent condition characterized by urgency and nocturia, imposes significant burdens on both quality of life and healthcare systems. Emerging evidence implicates systemic inflammation in OAB pathogenesis; however, the role of complete blood count (CBC)-derived inflammatory biomarkers remains underexplored. This cross-sectional study analyzed data from 35,394 participa...

From Algorithms to Insight: The Transformative Power of Artificial Intelligence and Machine Learning in Urological Cancer Research.

As we advance into a new era of oncological science, artificial intelligence (AI) is no longer a peripheral tool-it is a central agent of change [...]...

Predictors and associations of complications in ureteroscopy for stone disease using AI: outcomes from the FLEXOR registry.

We aimed to develop machine learning(ML) algorithms to evaluate complications of flexible ureteroscopy and laser lithotripsy(fURSL), providing a valid...

Signal-based AI-driven software solution for automated quantification of metastatic bone disease and treatment response assessment using Whole-Body Diffusion-Weighted MRI (WB-DWI) biomarkers in Advanced Prostate Cancer

We developed an AI-driven software solution to quantify metastatic bone disease from WB-DWI scans. Core technologies include: (i) a weakly-supervise...

Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking.

The extensive conformational dynamics of partially disordered proteins hinders the efficiency of traditional in-silico structure-based drug discovery ...

Development of a machine learning-based predictive risk model combining fatty acid metabolism and ferroptosis for immunotherapy response and prognosis in prostate cancer.

Prostate cancer (PCa) remains a leading cause of cancer-related mortality, necessitating robust prognostic models and personalized therapeutic strateg...

Segmentation of renal vessels on non-enhanced CT images using deep learning models.

OBJECTIVE: To evaluate the possibility of performing renal vessel reconstruction on non-enhanced CT images using deep learning models.

Effectiveness of eHealth for Medication Adherence in Renal Transplant Recipients: Systematic Review and Meta-Analysis.

BACKGROUND: As the optimal treatment for end-stage renal disease, kidney transplantation has proven instrumental in enhancing patient survival and qua...

Deep Learning-accelerated MRI in Body and Chest.

Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for bo...

Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays.

This paper introduces a versatile and efficient design methodology for optimizing wide-angle impedance matching (WAIM) configurations, enhancing the s...

Sex differences in bladder cancer: understanding biological and clinical implications.

Bladder cancer (BC) remains a significant global health concern, with substantial sex and racial disparities in incidence, progression, and outcomes. ...

Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques.

: Partial nephrectomy (PN) is the preferred option for treating localized cT1 renal cell carcinoma (RCC), as it preserves renal function in most patie...

Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI.

Chronic kidney disease (CKD) remains a significant global health burden, often diagnosed at advanced stages due to the limitations of traditional biom...

In silico machine learning-enabled detection of polycyclic aromatic hydrocarbons from contaminated soil.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their modified derivatives in contaminated soil is challenging due to ...

Lightweight and universal deep learning model for fast proton spot dose calculation at arbitrary energies.

To better integrate into processes like rapid adaptive planning and quality assurance, this study aims to propose a lightweight and universal proton s...

Application of BOLDMRIbased radiomics in differentiating malignant from benign renal tumors.

OBJECTIVES: Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) is a kind of non-invasive MRI technology which reflects the tissue bloo...

Robust Kidney Abnormality Segmentation: A Validation Study of an AI-Based Framework

Kidney abnormality segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments....

An artificial intelligence model for nuclear grading of clear cell renal cell carcinoma using whole slide images: a retrospective, multicentre, diagnostic study.

BACKGROUND: The pathological assessment of International Society of Urological Pathology (ISUP) nuclear grading is crucial for the management of clear...

High throughput recurrent pregnancy loss screening: urine metabolic fingerprints LDI-MS and machine learning.

Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility am...