Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies VI: Voiding Cystourethrography and Cystography

Imaging Studies VI: Voiding Cystourethrography and Cystography

Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...

Urinary Bladder

Urinary Bladder

The urinary bladder is a hollow, muscular sac that temporarily stores urine before it is expelled from the body. It can hold approximately 600 mL of urine prior to micturition. The bladder is retroperitoneal and located behind the pubic symphysis in the pelvic floor.
In males, the bladder is situated in front of the rectum, while in females, it is positioned anterior to the vagina and uterus. The bladder floor contains an inverted triangular area called the trigone, defined by the two ureteric...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Predictors for Positive Repeated Urine Culture in Patients with Negative Initial Urine Culture before Endoscopic Lithotripsy.

Surgical infections·2026

Same author

Carbon Nanotube-Aramid Fiber Enabled Dielectric Dispersion in Paper Composites for Ultrabroadband Lightweight Absorbers.

Nano letters·2026

Same author

Large multicenter validation of urine RNA profile for urothelial carcinoma detection and surveillance.

The Journal of clinical investigation·2026

Same author

Clinical Utility of PROSTest: A Prospective Study Suggesting Reduction in Unnecessary MRI and Biopsy in Men Evaluated for Prostate Cancer.

Cancers·2026

Same author

CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.

Scientific data·2026

Same author

Loss of Lean Mass in Rheumatoid Arthritis Is Associated With Loss of Total and Visceral Fat.

Journal of cachexia, sarcopenia and muscle·2026

Same journal

Patient-Reported Symptom Burden Among Thyroid Cancer Survivors: Retrospective Cohort Study.

JCO clinical cancer informatics·2026

Same journal

Rule-Based Algorithm to Identify Recurrent Non-Hodgkin Lymphoma in Electronic Health Data.

JCO clinical cancer informatics·2026

Same journal

Bayesian Methods for Subgroup Efficacy and Safety: Application to Japanese Patients in JAVELIN Renal 101.

JCO clinical cancer informatics·2026

Same journal

Effect of a Multidimensional Digital Health Intervention on Quality of Life in Breast Cancer Survivors: A Randomized Controlled Trial.

JCO clinical cancer informatics·2026

Same journal

Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?

JCO clinical cancer informatics·2026

Same journal

Machine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers.

JCO clinical cancer informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models

Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models

Published on: August 9, 2012

Efficient Augmented Intelligence Framework for Bladder Lesion Detection.

Okyaz Eminaga^1,2, Timothy Jiyong Lee^3,4, Mark Laurie^3,5

¹AI Vobis, Palo Alto, CA.

JCO Clinical Cancer Informatics

|September 29, 2023

Summary

This summary is machine-generated.

Developing artificial intelligence for bladder cancer detection is costly. This study shows that efficient deep learning models trained on an educational atlas can enable real-time bladder lesion identification.

More Related Videos

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

Published on: January 12, 2017

Related Experiment Videos

Last Updated: Jul 15, 2025

Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models

Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models

Published on: August 9, 2012

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

Published on: January 12, 2017

Area of Science:

Urology
Medical Imaging
Artificial Intelligence

Background:

Developing intelligence systems for bladder lesion detection is expensive.
There is a need for efficient strategies to create these systems.

Purpose of the Study:

To evaluate the efficacy of deep learning models for bladder lesion detection.
To identify computationally efficient models for real-time application.

Main Methods:

Four deep learning models (ConvNeXt, PlexusNet, MobileNet, SwinTransformer) were trained on an educational cystoscopy atlas (312 images).
Models were externally validated on 68 cystoscopy videos with pathologically confirmed regions of interest (ROIs).
Performance was assessed using specificity and sensitivity at frame, block, and ROI levels.

Main Results:

Specificity was comparable across models at frame (30.0%-44.8%) and block levels (56%-67%).
Sensitivity was high across models at block (100%) and ROI (100%) levels.
MobileNet and PlexusNet demonstrated greater computational efficiency for real-time ROI detection.

Conclusions:

An educational cystoscopy atlas aids in developing intelligence systems.
Efficient deep learning models facilitate the creation of real-time bladder lesion detection systems.