Correlation between anatomical findings and symptoms in women with pelvic organ prolapse using an artificial neural network analysis

The aim of the present study was to assess the relationship between lower urinary tract symptoms, anatomical findings, and baseline characteristics in women with pelvic organ prolapse (POP). A cross-sectional observational study was performed, enrolling consecutive women seeking cares for lower urinary tract symptoms (LUTS) with evidence of POP. Data regarding baseline characteristics, LUTS, and physical examination were gathered for each patient. Multivariate analysis (multiple linear regression (MLR)) and artificial neural networks (ANNs) were performed to design predicting models. A total of 1,344 women were included. Age, BMI, pelvic organ prolapse quantification (POP-Q) stage I, and previous surgery for urinary incontinence resulted predictors of urgency and stress incontinence. POP-Q stages III-IV were related to voiding dysfunction and POP symptoms. Age, BMI, and menopausal status resulted predictors for sexual dysfunction. Receiver operating characteristic comparison confirmed that ANNs were more accurate than MLRs in identifying predictors of LUTS. LUTS result from a fine interaction between baseline characteristics and anatomical findings. ANNs are valuable instrument for better understanding complex biological models.