Urology

Preliminary phantom study of four-dimensional computed tomographic angiography for renal artery mapping: Low-tube voltage and low-contrast volume imaging with deep learning-based reconstruction.

INTRODUCTION: A hybrid angio-CT system with 320-row detectors and deep learning-based reconstruction (DLR), provides additional imaging via 4D-CT angiography (CTA), potentially shortening procedure time and reducing DSA acquisitions, contrast media, and radiation dose. This study evaluates the feasibility of low-tube voltage 4D-CTA with low-contrast volume and DLR for selective renal artery emboli...

Machine Learning Analysis of Videourodynamics to Predict Incident Hydronephrosis in Patients With Spina Bifida.

PURPOSE: Variability in the interpretation of videourodynamics studies limits reliable classification of kidney injury risk for patients with spina bifida. We developed machine learning models to predict incident hydronephrosis in patients with spina bifida using videourodynamics data.

Circular RNAs: driving forces behind chemoresistance and immune evasion in bladder cancer.

Bladder cancer (BCa) is characterized by recurring relapses and the emergence of chemoresistance, especially against standard treatments like cisplati...

DASNet: A Convolutional Neural Network with SE Attention Mechanism for ccRCC Tumor Grading.

Clear cell renal cell carcinoma (ccRCC) is the most common form of renal cell carcinoma in adults, comprising approximately 80% of cases. The lethalit...

Foundation Model and Radiomics-Based Quantitative Characterization of Perirenal Fat in Renal Cell Carcinoma Surgery.

RATIONALE AND OBJECTIVES: To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma.

A new era in nephrology: the role of super-resolution microscopy in research, medical diagnostic, and drug discovery.

For decades, electron microscopy has been the primary method to visualize ultrastructural details of the kidney, including podocyte foot processes and...

Point-of-Care Sensors and Medical Internet-of-Things Technologies to Manage Catheter-Associated Urinary Tract Infections in the Intensive Care Unit.

This article examines the current technologies used with indwelling urinary catheters to monitor potential catheter-associated urinary tract infection...

A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.

This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-ri...

Evaluating the prognostic significance of artificial intelligence-delineated gross tumor volume and prostate volume measurements for prostate radiotherapy.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI...

Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches.

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to ...

Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study.

To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients i...

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.

We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP) in vesicoureteral reflux, train a model to predi...

Machine Learning and Mendelian Randomization Reveal a Tumor Immune Cell Profile for Predicting Bladder Cancer Risk and Immunotherapy Outcomes.

This study's objective was to develop predictive models for bladder cancer (BLCA) using tumor infiltrated immune cell (TIIC)-related genes. Multiple R...

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning.

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. ...

Using machine learning for predicting cancer-specific mortality in bladder cancer patients undergoing radical cystectomy: a SEER-based study.

BACKGROUND: Accurately assessing the prognosis of bladder cancer patients after radical cystectomy has important clinical and research implications. C...

Artificial intelligence in chronic kidney disease management: a scoping review.

Chronic kidney disease (CKD) is a major public health problem worldwide associated with cardiovascular disease, renal failure, and mortality. To effe...

Ethical aspects of artificial intelligence: what urologists need to know.

PURPOSE OF REVIEW: The integration of artificial intelligence in urology presents both transformative opportunities and ethical dilemmas. As artificia...

The advance of artificial intelligence in outpatient urology: current applications and future directions.

PURPOSE OF REVIEW: Prudent integration of artificial intelligence (AI) into outpatient urology has already begun to revolutionize clinical workflows, ...

Generative artificial intelligence powered chatbots in urology.

PURPOSE OF REVIEW: The integration of artificial intelligence (AI) into healthcare has significantly impacted the way healthcare is delivered, particu...

Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.

OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcom...