Deep Learning-Assisted Two-Dimensional Transperineal Ultrasound for Analyzing Bladder Neck Motion in Women With Stress Urinary Incontinence.

ABSTRACT

BACKGROUND: No universally recognized transperineal ultrasound parameters are available for evaluating stress urinary incontinence. The information captured by commonly used perineal ultrasound parameters is limited and insufficient for a comprehensive assessment of stress urinary incontinence. Although bladder neck motion plays a major role in stress urinary incontinence, objective and visual methods to evaluate its impact on stress urinary incontinence remain lacking. OBJECTIVE: To use a deep learning-based system to evaluate bladder neck motion using two-dimensional transperineal ultrasound videos, exploring motion parameters for diagnosing and evaluating stress urinary incontinence. We hypothesized that bladder neck motion parameters are associated with stress urinary incontinence and are useful for stress urinary incontinence diagnosis and evaluation. STUDY DESIGN: This retrospective study including 217 women involved the following parameters maximum and average speeds of bladder neck descent, ß angle, urethral rotation angle, and duration of the Valsalva maneuver. The fitted curves were derived to visualize bladder neck motion trajectories. Comparative analyses were conducted to assess these parameters between stress urinary incontinence and control groups. Logistic regression and receiver operating characteristic curve analyses were employed to evaluate the diagnostic performance of each motion parameter and their combinations for stress urinary incontinence. RESULTS: Overall, 173 women were enrolled in this study (82, stress urinary incontinence group; 91, control group). No significant differences were observed in the maximum and average speeds of bladder neck descent and in the speed variance of bladder neck descent. The maximum and average speed of the ß and urethral rotation angles were faster in the stress urinary incontinence group than in the control group (151.2 vs 109.0 mm/s, P=0.001; 6.0 vs 3.1 mm/s, P <0.001; 105.5 vs 69.6 mm/s, P <0.001; 10.1 vs 7.9 mm/s, P=0.011, respectively). The speed variance of the ß and urethral rotation angles were higher in the stress urinary incontinence group (844.8 vs 336.4, P <0.001; 347.6 vs 131.1, P <0.001, respectively). The combination of the average speed of the ß angle, maximum speed of the urethral rotation angle, and duration of the Valsalva maneuver demonstrated a strong diagnostic performance (area under the curve, 0.87). When 0.481*ß anglea + 0.013*URAm + 0.483*Dval = 7.405, the diagnostic sensitivity was 70% and specificity was 92%, highlighting the significant role of bladder neck motion in stress urinary incontinence, particularly changes in the speed of the ß and urethral rotation angles. CONCLUSIONS: A system utilizing deep learning can describe the motion of the bladder neck in women with stress urinary incontinence during the Valsalva maneuver, making it possible to visualize and quantify bladder neck motion on transperineal ultrasound. The speeds of the ß and urethral rotation angles and duration of the Valsalva maneuver were relatively reliable diagnostic parameters.