LAUSR

OBJECTIVE 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. METHODS 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. RESULTS 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%. CONCLUSION 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. SIGNIFICANCE 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.