Prediction of pelvic organ prolapse using an artificial neural network.
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.


METHODS
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.


CONCLUSIONS
Feedforward ANN modeling is applicable to the identification and prediction of POP.
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Medical University of South Carolina, Charleston, SC, USA.
Christopher J
CJ
Robinson
Steven
S
Swift
Donna D
DD
Johnson
Jonas S
JS
Almeida
D000368:Aged; D000465:Algorithms; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity; D014596:Uterine Prolapse / Q000175:diagnosis*; D014636:Valsalva Maneuver