Artificial intelligence-based prediction of postoperative voiding dysfunction following midurethral sling

Midurethral sling (MUS) surgery is the standard procedure for stress urinary incontinence, offering favorable long-term outcomes. Despite its success, postoperative voiding dysfunction - manifesting as urinary retention, elevated post-void residuals (PVRs), slow or weak stream, hesitancy, or the need for prolonged catheterization - remains a clinically significant complication that is difficult to predict using traditional risk stratification. Conventional methods, including history, physical examination, PVR measurement, and selective urodynamics, provide limited individualized predictive power and fail to integrate the complex interplay of patient, anatomical, and surgical factors.

Artificial intelligence and machine learning offer a promising approach to addressing this gap by analyzing multidimensional datasets to forecast individualized risk and guide perioperative management. While artificial intelligence applications in pelvic floor surgery have demonstrated utility in predicting general surgical outcomes, validated models specifically targeting post-MUS voiding dysfunction are lacking. Development of such predictive tools will require high-quality multicenter datasets, careful algorithm selection, and validation.

Integrating artificial intelligence into clinical practice has the potential to enhance preoperative counseling, support shared decision-making, optimize surgical planning, and ultimately improve patient outcomes. This perspective highlights the unmet need for predictive modeling of voiding dysfunction after MUS, outlines the promise of artificial intelligence, and emphasizes the importance of responsible implementation.