Design of a rapid diagnostic model for bladder compliance based on real-time intravesical pressure monitoring system

The diagnosis of bladder dysfunction for children depends on the confirmation of abnormal bladder shape and bladder compliance. The existing gold standard needs to conduct voiding cystourethrogram (VCUG) examination and urodynamic studies (UDS) examination on patients separately. To reduce the time...

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Bibliographic Details
Published in:Computers in biology and medicine 2022-02, Vol.141, p.105173-105173, Article 105173
Main Authors: Ge, Zicong, Tang, Liangfeng, Peng, Yunsong, Zhang, Mingming, Tang, Jialong, Yang, Xiaodong, Li, Yu, Wu, Zhongyi, Yuan, Gang
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian analysis
Bladder
Catheters
Child
Children
Children & youth
Classification
Clinical trials
Cooperation
Data collection
Diagnosis
Display devices
Feature extraction
Humans
Injury prevention
Inspection
Learning algorithms
Machine Learning
Medical diagnosis
Monitoring systems
Patients
Pediatrics
Performance evaluation
Pressure
Real time
Sensors
Support Vector Machine
Support vector machines
Ultrasonic imaging
Urinary Bladder - diagnostic imaging
Urinary tract infections
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Summary:The diagnosis of bladder dysfunction for children depends on the confirmation of abnormal bladder shape and bladder compliance. The existing gold standard needs to conduct voiding cystourethrogram (VCUG) examination and urodynamic studies (UDS) examination on patients separately. To reduce the time and injury of children's inspection, we propose a novel method to judge the bladder compliance by measuring the intravesical pressure during the VCUG examination without extra UDS. Our method consisted of four steps. We firstly developed a single-tube device that can measure, display, store, and transmit real-time pressure data. Secondly, we conducted clinical trials with the equipment on a cohort of 52 patients (including 32 negative and 20 positive cases). Thirdly, we preprocessed the data to eliminate noise and extracted features, then we used the least absolute shrinkage and selection operator (LASSO) to screen out important features. Finally, several machine learning methods were applied to classify and predict the bladder compliance level, including support vector machine (SVM), Random Forest, XGBoost, perceptron, logistic regression, and Naive Bayes, and the classification performance was evaluated. 73 features were extracted, including first-order and second-order time-domain features, wavelet features, and frequency domain features. 15 key features were selected and the model showed promising classification performance. The highest AUC value was 0.873 by the SVM algorithm, and the corresponding accuracy was 84%. We designed a system to quickly obtain the intravesical pressure during the VCUG test, and our classification model is competitive in judging patients’ bladder compliance. This could facilitate rapid auxiliary diagnosis of bladder disease based on real-time data. The promising result of classification is expected to provide doctors with a reliable basis in the auxiliary diagnosis of some bladder diseases prior to UDS. •Α measuring system to monitor patients' real-time bladder pressure at the same time as the VCUG test with no extra invasiveness is presented, which could replace the UDS test and help urologists for diagnosis. The system is low-cost, convenient, and fast. A data set of bladder pressure in a child patient was collected with this system.•A variety of machine learning methods containing SVM, XGBoost, random forest and other classifiers were used in our data set, and samples were classified by bladder compliance. The classification re
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2021.105173