Urology

The world is only ten years old: The dawn of artificial intelligence in urologic oncology.

Inspection of visible components in urine based on deep learning.

PURPOSE: Urinary particles are particularly important parameters in clinical urinalysis, especially for the diagnosis of nephropathy. Therefore, it is highly important to precisely detect urinary particles in the clinical setting. However, artificial microscopy is subjective and time consuming, and various previous detection algorithms lack the adequate accuracy. In this study, a method is propose...

Utilization of a Peritoneal Interposition Flap to Prevent Symptomatic Lymphoceles After Robotic Radical Prostatectomy and Bilateral Pelvic Lymph Node Dissection.

The peritoneal interposition flap (PIF) has been shown to prevent postoperative symptomatic lymphocele (SL) formation after robot-assisted radical pr...

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including p...

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

BACKGROUND: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an...

Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.

PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challe...

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of pro...

Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

OBJECTIVE: Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using mul...

Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods.

In diagnostics of infectious diseases, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) can be applied for ...

Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

BACKGROUND: Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have be...

Re: Artificial Intelligence for Diagnosis and Grading of Prostate Cancer in Biopsies: A Population-based, Diagnostic Study.

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microsc...

SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning.

In recent years, accumulating studies have shown that long non-coding RNAs (lncRNAs) not only play an important role in the regulation of various biol...

How to do it: a robotic kidney autotransplant.

We describe Australia's first reported case of robotic kidney autotransplantation for a complex renal artery aneurysm. It is potentially a safe, minim...

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.

IMPORTANCE: Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for onco...

Re: Geraldine Pignot, Antoine Thiery-Vuillemin, Jochen Walz, et al. Nephrectomy After Complete Response to Immune Checkpoint Inhibitors for Metastatic Renal Cell Carcinoma: A New Surgical Challenge? Eur Urol. In press. https://doi.org/10.1016/j.eururo.2019.12.018: The Next Surgical Frontier in Kidney Cancer: Nephrectomy After Immune Checkpoint Inhibition.

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using co...

Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortal...

Utilizing machine learning to discern hidden clinical values from big data in urology.

Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommendations.

PURPOSE: Urological oncologists have difficulty providing optimal personalized care due to rapid alterations in scientific research results, medical a...