Urology

Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a sma...

Natural language processing to identify ureteric stones in radiology reports.

INTRODUCTION: Natural language processing (NLP) is an emerging tool which has the ability to automate data extraction from large volumes of unstructured text. One of the main described uses of NLP in radiology is cohort building for epidemiological studies. This study aims to assess the accuracy of NLP in identifying a group of patients positive for ureteric stones on Computed Tomography - Kidneys...

Artificial intelligence in cancer imaging: Clinical challenges and applications.

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a uniqu...

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tom...

A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.

With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment ...

Argentinian multicenter study on urinary tract infections due to Streptococcus agalactiae in adult patients.

INTRODUCTION: Streptococcus agalactiae (group B streptococcus, GBS) is a recognized urinary pathogen both in males and pregnant or non-pregnant women....

Editorial Comment to Utility of robot-assisted radical cystectomy with intracorporeal urinary diversion for muscle-invasive bladder cancer.

Detection of Extended-Spectrum β-Lactamase-Producing Escherichia coli Using Infrared Microscopy and Machine-Learning Algorithms.

The spread of multidrug resistant bacteria has become a global concern. One of the most important and emergent classes of multidrug-resistant bacteria...

Defining and discriminating responders from non-responders following transurethral resection of the prostate.

BACKGROUND: Transurethral resection of the prostate (TURP) is the reference standard surgical treatment for lower urinary tract symptoms (LUTS) relate...

Prostate cancer classification with multiparametric MRI transfer learning model.

PURPOSE: Prostate cancer classification has a significant impact on the prognosis and treatment planning of patients. Currently, this classification i...

Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.

For exploration of chemical and biological systems, the combined quantum mechanics and molecular mechanics (QM/MM) and machine learning (ML) models ha...

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia.

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 h...

Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results.

RATIONALE AND OBJECTIVES: Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pr...

Sleep Duration and Diabetic Kidney Disease.

Abnormally short or long durations of sleep have been proposed as a risk factors for diabetes and its micro- and macro-vascular complications. Howeve...

Using machine learning and an ensemble of methods to predict kidney transplant survival.

We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. Th...

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

BACKGROUND: Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI).

Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quan...

Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.

PURPOSE: We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urogra...

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.

OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predic...

Clinical intelligence: New machine learning techniques for predicting clinical drug response.

Predicting the response, or sensitivity, of a clinical drug to a specific cancer type is an important research problem. By predicting the clinical dru...