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.
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.
Authors: Christopher J Robinson; Elizabeth G Hill; Mark C Alanis; Eugene Y Chang; Donna D Johnson; Jonas S Almeida Journal: Hypertens Pregnancy Date: 2010 Impact factor: 2.108
Authors: Xinyi Wang; Da He; Fei Feng; James A Ashton-Miller; John O L DeLancey; Jiajia Luo Journal: Int Urogynecol J Date: 2022-01-27 Impact factor: 1.932
Authors: Marijke C Ph Slieker-ten Hove; Annelies L Pool-Goudzwaard; Marinus J C Eijkemans; Regine P M Steegers-Theunissen; Curt W Burger; Mark E Vierhout Journal: Int Urogynecol J Pelvic Floor Dysfunct Date: 2009-05-15