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Related Experiment Video

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The Use of Cystometry in Small Rodents: A Study of Bladder Chemosensation

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Advancing Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Diagnosis: A Comparative Analysis of Machine Learning

Joseph J Janicki¹, Bernadette M M Zwaans^2,3, Sarah N Bartolone²

¹Underactive Bladder Foundation, Pittsburgh, PA 15235, USA.

Diagnostics (Basel, Switzerland)

|December 17, 2024

Summary

Machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) were improved using AutoML, achieving high accuracy with urinary biomarker data. This advancement offers a more reliable method for IC/BPS classification.

Keywords:

biomarker bladder inflammation interstitial cystitis machine learning urine

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Area of Science:

Urology
Biomedical Informatics
Machine Learning

Background:

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a challenging urinary bladder disorder.
Accurate diagnosis of IC/BPS is crucial for effective patient management.
Current diagnostic methods may lack precision, necessitating improved approaches.

Purpose of the Study:

To enhance machine learning models for IC/BPS diagnosis.
To compare classical machine learning techniques with advanced AutoML methods.
To leverage urinary biomarker data and patient-reported outcomes for improved diagnostic accuracy.

Main Methods:

Applied logistic regression, SVM, random forests, k-NN, and AutoGluon to predict IC/BPS.
Utilized biomarker data from 2009 participants across two nationwide studies.
Compared performance of classical ML and AutoML approaches.

Main Results:

Expanded datasets improved model performance metrics.
AutoML methods demonstrated superior accuracy over classical techniques.
Top models achieved a receiver-operating characteristic area under the curve (ROC-AUC) up to 0.96.

Conclusions:

This study achieved improved model performance for IC/BPS diagnosis compared to prior research.
Objective urinary biomarker levels were key in the top-performing binary classification model.
These advancements pave the way for a reliable IC/BPS classification model.