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From Flow to Feature Using a Proof-of-Concept Spectral-Driven Machine Learning Approach Using Smart Urinary and

Leonardo Poggi^1,2, Anastasia Meckler³, Sebastian Künert³

¹Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen University Hospital, Hufelandstraße 55, Essen, 45147, Germany, 43 20172377817.

JMIR Medical Informatics

|May 14, 2026

Summary

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Urodynamic Studies: Uroflowmetry

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Uroflowmetry is a non-invasive urodynamic test designed to measure various aspects of urination, including volume, flow rate, and the time to void. This test is crucial for diagnosing and assessing conditions such as bladder outlet obstruction, bladder dysfunction, incomplete bladder emptying, incontinence, and urinary tract blockages caused by benign prostatic hyperplasia (BPH) and urethral strictures.Pre-Test Instructions:Before a uroflowmetry test, patients are typically advised to drink...

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This summary is machine-generated.

This study introduces a smart catheter system using spectral data and machine learning (ML) for real-time fluid analysis. The system accurately differentiates pathological from healthy fluids, improving diagnostics without manual preprocessing.

Area of Science:

Biomedical engineering
Medical diagnostics
Machine learning applications

Background:

Current catheter systems offer limited diagnostic value and are prone to errors.
Existing machine learning (ML) methods require complex preprocessing, hindering real-time analysis.

Purpose of the Study:

To develop and evaluate a fully automated, real-time diagnostic approach for smart urinary and drainage catheter systems.
To differentiate pathological from healthy excreted fluids using spectral data and ML without manual preprocessing.

Main Methods:

Analyzed 454 surgical drainage fluid and 401 urine samples using smart catheters with mini-spectrometer sensors.
Collected spectral data were processed using random forest, partial least squares discriminant analysis regression, and convolutional neural network (CNN) models.

Keywords:

AI in medicine artificial intelligence digital health care early warning systems real-time monitoring spectroscopy surgical drains urine diagnostics

Related Experiment Videos

Last Updated: May 16, 2026

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Published on: July 29, 2016

ML models extracted features to differentiate pathological from healthy samples based on biomarkers.

Main Results:

All three ML approaches showed promising results.
CNN models achieved the highest accuracy, with Matthews correlation coefficient scores of 0.83 for hemoglobin and 0.81 for bilirubin.
The models successfully differentiated pathological from healthy drainage and urine samples using spectral features.

Conclusions:

Spectral-driven ML holds significant potential for smart catheter systems.
This approach enables real-time, noninvasive fluid analysis for improved diagnostics and personalized patient care.
Further research will focus on optimizing ML models for this application.