Deep Learning and Numerical Analysis for Bladder Outflow Obstruction and Detrusor Underactivity Diagnosis in Men: A Novel Urodynamic Evaluation Scheme.

Abstract

Objectives: To automatically identify and diagnose bladder outflow obstruction (BOO) and detrusor underactivity (DUA) in male patients with lower urinary tract symptoms through urodynamics exam.

Patients and methods: We performed a retrospective review of 1949 male patients who underwent a urodynamic study at two institutions. Deep Convolutional Neural Networks scheme combined with a short-time Fourier transform algorithm was trained to perform an accurate diagnosis of BOO and DUA, utilizing five-channel urodynamic data (consisting of uroflowmetry, urine volume, intravesical pressure, abdominal pressure, and detrusor pressure). We used fivefold cross-validation, constructing training and internal test sets from 1725 patients from Renmin Hospital of Wuhan University (RHWU) at a 4:1 ratio, and used an independent external validation set consisting of 224 patients from The Central Hospital of Wuhan (TCHO) to build and evaluate the DI model. We further conducted subgroup analyses to provide a more detailed description of the AI model's interpretability regarding urodynamics.

Results: The AUC scores of BOO and DUA, which were measured through the STFT-based deep learning method, were 0.945 ± 0.020 and 0.929 ± 0.039 in RHWU and 0.881 and 0.850 in TCHO, respectively. The diagnostic efficiency of other subgroup analyses and indicators was also effective.

Conclusion: In this study, the proposed deep neural network combined with the short-time Fourier transform method is robust and feasible for interpreting the results of urodynamics in men and has the potential for application to assist clinicians in real clinical settings.