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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...

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Related Experiment Video

Updated: Jun 27, 2025

Urinary Bladder Distention Evoked Visceromotor Responses as a Model for Bladder Pain in Mice

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Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions.

Laura E Lamb¹, Joseph J Janicki², Sarah N Bartolone³

¹Oakland University William Beaumont School of Medicine, Rochester, MI; Strata Oncology, Ann Arbor, MI.

|April 27, 2024

Summary

This summary is machine-generated.

Machine learning improves interstitial cystitis (IC) diagnosis by combining patient-reported outcomes and urine biomarkers. The novel IC-PIS score enhances diagnostic accuracy for IC/bladder pain syndrome.

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Published on: August 13, 2015

Area of Science:

Urology
Biomarkers
Machine Learning

Background:

Interstitial cystitis (IC)/bladder pain syndrome (BPS) diagnosis requires improvement.
Current diagnostic methods lack sufficient accuracy.
Novel approaches are needed to enhance IC/BPS classification.

Purpose of the Study:

To develop an improved IC risk classification model using machine learning.
To enhance the diagnostic accuracy of IC/BPS.
To create a novel classification model integrating patient-reported outcomes (PROs) and urine biomarkers.

Main Methods:

Developed a machine learning predictive classification model (IC-PIS Score).
Utilized 1264 crowdsourced urine samples and PROs, plus 296 academic center samples.
Measured urinary cytokine biomarker levels and compared models.

Main Results:

The top model combined biomarker measurements and PROs, achieving an AUC of 0.87.
This model outperformed PROs alone (AUC=0.83) and biomarkers alone (AUC=0.58).
The combination of biomarkers and PROs yielded improved predictive effects for IC.

Conclusions:

IC-PIS is a novel classification model enhancing IC/BPS diagnostic accuracy.
The model integrates PROs and urine biomarkers.
Large-scale crowdsourced data and ambient shipping methods support the findings' robustness and scalability.