Urology

Latest AI and machine learning research in urology for healthcare professionals.

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Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.

Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been...

Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in...

Measuring kidney stone volume - practical considerations and current evidence from the EAU endourology section.

PURPOSE OF REVIEW: This narrative review provides an overview of the use, differences, and clinical ...

Incorporating indirect MRI information in a CT-based deep learning model for prostate auto-segmentation.

BACKGROUND AND PURPOSE: Computed tomography (CT) imaging poses challenges for delineation of soft ti...

An Integrative Machine Learning Model for Predicting Early Safety Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation.

: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic s...

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of pros...

Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.

BACKGROUND: Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subt...

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial ...

Navigating advanced renal cell carcinoma in the era of artificial intelligence.

BACKGROUND: Research has helped to better understand renal cell carcinoma and enhance management of ...

Multi-task learning for automated contouring and dose prediction in radiotherapy.

. Deep learning (DL)-based automated contouring and treatment planning has been proven to improve th...

Unsupervised neural network-based image stitching method for bladder endoscopy.

Bladder endoscopy enables the observation of intravesical lesion characteristics, making it an essen...

Redefining prostate cancer care: innovations and future directions in active surveillance.

PURPOSE OF REVIEW: This review provides a critical analysis of recent advancements in active surveil...

A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.

Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capab...

Key RNA-binding proteins in renal fibrosis: a comprehensive bioinformatics and machine learning framework for diagnostic and therapeutic insights.

BACKGROUND: Renal fibrosis is a critical factor in chronic kidney disease progression, with limited ...

Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic models.

The objective of this study was to employ machine learning to identify shared differentially express...

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.

Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and ...

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at signif...

Integrating radiomics and gene expression by mapping on the image with improved DeepInsight for clear cell renal cell carcinoma.

BACKGROUND: Radiomics analysis extracts high-dimensional features from medical images, which are use...

Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation.

Semi-supervised learning based on consistency learning offers significant promise for enhancing medi...

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