Urology

BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability.

Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great chal...

Propensity matched analysis of robotic and laparoscopic operations for mid-low rectal cancer: short-term comparison of anal function and oncological outcomes.

Laparoscopic surgery for rectal cancer, while in some respects equivalent or even preferable to open surgery, is challenged in specific conditions where the tumor is located in the middle and lower third of the rectum. Robotic surgery equipped with a superior arm of machinery and gained better visualization can compensate for the deficiency of the laparoscopic approach. This study adopted a propen...

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation.

BACKGROUND AND PURPOSE: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radio...

Robot-assisted radical cystectomy with intracorporeal urinary diversion: a Danish 11-year series.

OBJECTIVES: To evaluate the oncological and perioperative outcomes from a large, single-centre, robot-assisted radical cystectomy (RARC) cohort perfor...

A reinforcement federated learning based strategy for urinary disease dataset processing.

Urinary disease is a complex healthcare issue that continues to grow in prevalence. Urine tests have proven valuable in identifying conditions such as...

The learning curves of major laparoscopic and robotic procedures in urology: a systematic review.

BACKGROUND: Urology has been at the forefront of adopting laparoscopic and robot-assisted techniques to improve patient outcomes. This systematic revi...

Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.

PURPOSE: This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate c...

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRC...

Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer.

PURPOSE: With the increased use of focal radiation dose escalation for primary prostate cancer (PCa), accurate delineation of gross tumor volume (GTV)...

Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning.

Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. This retrospective study included 259 s...

Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease.

Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which c...

Robotic-assisted double lingual mucosal graft ureteroplasty for multifocal ureteral strictures: Case report and technical description.

BACKGROUND: Ureteroplasty with a single onlay graft for proximal ureter stricture has been widely used in the clinic. However, robotic ureteroplasty w...

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impa...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the...

Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging.

PURPOSE: Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer ...

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the man...

ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis.

BACKGROUND AND OBJECTIVE: Prostate cancer is one of the most common diseases affecting men. The main diagnostic and prognostic reference tool is the G...

A single-center retrospective comparative analysis of urinary continence in robotic prostatectomy with a combination of umbilical ligament preservation and Hood technique.

OBJECTIVES: Data available on the effect of the recently developed Hood technique and its modified iterations in robot-assisted radical prostatectomy ...

Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were...

Robot-assisted Versus Conventional Laparoscopic Radical Prostatectomy: A Systematic Review and Meta-analysis of Randomised Controlled Trials.

CONTEXT: Robot-assisted radical prostatectomy (RARP) has largely replaced conventional laparoscopic radical prostatectomy (LRP) even though the costs ...