Urology

Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.

Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have ...

Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study.

AIM: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa).

Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning.

PURPOSE: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning...

Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method.

BACKGROUND: Kidney allocation is a multi-criteria and complex decision-making problem, which should also consider ethical issues in addition to the me...

Assessment of Polyethylene Glycol Hydrogel Spacer and Its Effect on Rectal Radiation Dose in Prostate Cancer Patients Receiving Proton Beam Radiation Therapy.

PURPOSE: To assess the efficacy of placing a polyethylene glycol (PEG) spacing hydrogel in patients undergoing proton beam radiation therapy for prost...

Growth Inhibitory and Pro-Apoptotic Effects of Ornamental Pomegranate Extracts in Du145 Human Prostate Cancer Cells.

Prostate cancer is the most common form of cancer in the male. Epidemiological studies have associated increased cancer incidence with reduced consum...

Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolution...

Characteristics of urodynamic study parameters associated with intermediate-term continence after robot-assisted radical prostatectomy in elderly patients.

PURPOSE: To investigate the relationship between urodynamic study (UDS) data and recovery of urinary incontinence (UI) in elderly patients who underwe...

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of d...

Interventions to Improve Blood Pressure Control Among Socioeconomically Disadvantaged Patients With CKD: Kidney Awareness Registry and Education Pilot Randomized Controlled Trial.

RATIONALE & OBJECTIVE: Sustainable interventions that enhance chronic kidney disease (CKD) management are not often studied in safety-net primary care...

Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.

OBJECTIVE: To present a deep learning-based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (M...

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

OBJECTIVE: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (<...

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection.

OBJECTIVES: To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate can...

Caveolae and scaffold detection from single molecule localization microscopy data using deep learning.

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release fa...

Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models.

INTRODUCTION: Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of ...

Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.

BACKGROUND: A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet ...

Prediction model development of late-onset preeclampsia using machine learning-based methods.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its predict...

Adaptive Resonance Theory in the time scales calculus.

Engineering applications of algorithms based on Adaptive Resonance Theory have proven to be fast, reliable, and scalable solutions to modern industria...

A Cancer Survival Prediction Method Based on Graph Convolutional Network.

BACKGROUND AND OBJECTIVE: Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and...

Hospital readmissions after limited vs. extended lymph node dissection during open and robot-assisted radical prostatectomy.

PURPOSE: Differences exist concerning when and how to perform lymph node dissection (LND) during radical prostatectomy due to lack of high-grade evide...