A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep Learning

Abstract

Objective: To develop two intelligent diagnosis models of detrusor overactivity (DO) based on deep learning to assist doctors no longer heavily rely on visual observation of urodynamic study (UDS) curves.

Methods: UDS curves of 92 patients were collected during 2019. We constructed two DO event recognition models based on convolutional neural network (CNN) with 44 samples, and tested the model performance with the remaining 48 samples by comparing other four classical machine learning models. During the testing phase, we developed a threshold screening strategy to quickly filter out suspected DO event segments in each patient's UDS curve. If two or more DO event fragments are determined to be DO by the diagnostic model, the patient is diagnosed as having DO.

Results: We extracted 146 DO event samples and 1863 non-DO event samples from the UDS curves of 44 patients to train CNN models. Through 10-fold cross-validation, the training accuracy and validation accuracy of our models achieved the highest accuracy. In the model testing phase, we used a threshold screening strategy to quickly screen out the suspected DO event samples in the UDS curve of another 48 patients, and then input them into the trained models. Finally, the diagnostic accuracy of patients without DO and patients with DO was 78.12% and 100%, respectively.

Conclusion: Under the available data, the accuracy of the DO diagnostic model based on CNN is satisfactory. With the increase of the amount of data, the deep learning model is likely to have better performance.

Clinical trial registration: This experiment was certified by the Chinese Clinical Trial Registry (ChiCTR2200063467).