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Updated: Jul 4, 2026
Anogenital Distance and Perineal Measurements of the Pelvic Organ Prolapse (POP) Quantification System
Published on: September 20, 2018
Prediction of pelvic organ prolapse using an artificial neural network
Christopher J Robinson1, Steven Swift, Donna D Johnson
1Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Medical University of South Carolina, Charleston, SC, USA.
Objective:
The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support.
Study Design:
Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set.
Results:
Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification.
Conclusion:
Feedforward ANN modeling is applicable to the identification and prediction of POP.
