Urology

Digital Pattern Recognition for the Identification and Classification of Hypospadias Using Artificial Intelligence vs Experienced Pediatric Urologist.

OBJECTIVE: To improve hypospadias classification system, we hereby, show the use of machine learning/image recognition to increase objectivity of hypospadias recognition and classification. Hypospadias anatomical variables such as meatal location, quality of urethral plate, glans size, and ventral curvature have been identified as predictors for postoperative outcomes but there is still significan...

A deep learning network-assisted bladder tumour recognition under cystoscopy based on Caffe deep learning framework and EasyDL platform.

BACKGROUND: Cystoscopy plays an important role in the diagnosis of bladder tumours. As a typical representative of the deep learning algorithm, the convolutional neural network has shown great advantages in the field of image recognition and segmentation.

Periprostatic fat thickness quantified by preoperative magnetic resonance imaging is an independent risk factor for upstaging from cT1/2 to pT3 in robot-assisted radical prostatectomy.

OBJECTIVES: To analyze the correlation between periprostatic fat thickness on multiparametric magnetic resonance imaging and upstaging from cT1/2 to p...

Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population.

Background The performance of a deep learning algorithm for lung cancer detection on chest radiographs in a health screening population is unknown. Pu...

Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency.

The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characteri...

The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer.

OBJECTIVE: Bone scintigraphy has often been used to evaluate bone metastases. Its functionality is evident in detecting bone metastasis in patients wi...

Re: Robot-Assisted Radical Prostatectomy in Patients with a History of Holmium Laser Enucleation of the Prostate: The Indiana University Experience.

Re: Robot-Assisted Continent Urinary Diversion According to the Mitrofanoff Principle: Results of a Bicentric Study.

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality...

Radiomics for Gleason Score Detection through Deep Learning.

Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer i...

Sensor-based indicators of performance changes between sessions during robotic surgery training.

Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. Th...

Digital Biopsy with Fluorescence Confocal Microscope for Effective Real-time Diagnosis of Prostate Cancer: A Prospective, Comparative Study.

BACKGROUND: A microscopic analysis of tissue is the gold standard for cancer detection. Hematoxylin-eosin (HE) for the reporting of prostate biopsy (P...

Re: Oncological outcome according to attainment of pentafecta after robot-assisted radical cystectomy in patients with bladder cancer in the multicentre KORARC database. BJU Int 2020 July 18. DOI: 10.1111/ bju.15178.

Urological and sexual function after robotic and laparoscopic surgery for rectal cancer: A systematic review, meta-analysis and meta-regression.

BACKGROUND: This systematic review sought to compare the urogenital functions after laparoscopic (LAP) and robotic (ROB) surgery for rectal cancer.

The Emerging Role of Artificial Intelligence in the Fight Against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has generated large volumes of clinical data that can be an invaluable resource towards answering a n...

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images.

PURPOSE: Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management ...

Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database.

Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is pr...

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise...

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks.

BACKGROUND AND OBJECTIVES: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective...

Patient reported outcome measures concerning urinary incontinence after robot assisted radical prostatectomy: development and validation of an online prediction model using clinical parameters, lower urinary tract symptoms and surgical experience.

The prediction of post-prostatectomy incontinence (PPI) after robot-assisted radical prostatectomy (RARP) depends on multiple clinical, anatomical and...