Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks

Purpose: To evaluate the performance of a backpropagation artificial neural network (ANN) in the diagnosis of men with lower urinary tract symptoms (LUTS) and to compare its performance to that of a traditional linear regression model. Materials and Methods: 1903 LUTS patients referred to the University Hospital Nijmegen between 1992 and 1998 received routine investigation, consisting of transrectal ultrasonography of the prostate, serum PSA measurement, assessment of symptoms and quality of life by the International Prostate Symptom Score (IPSS), urinary flowmetry with determination of maximum flow rate (Qmax), voided volume and post-void residual urine and full pressure flow studies (PFS). Using a three-layered backpropagation ANN with three hidden nodes, the outcome of PFS, quantified by the Abrams-Griffiths number (AG-number), was estimated based on all available non-invasive diagnostic test results plus patient age. The performance of the network was quantified using sensitivity, specificity and the area under the ROC-curve (AUC). The results of the neural network approach were compared to those of a linear regression analysis. Results: Prostate volume, Qmax, voided volume and post void residual urine showed substantial predictive value concerning the outcome of PFS. Patient age, PSA-level, IPSS and Quality of life did not add to that prediction. Using a cut-off value in predicted and true AG-numbers of 40 cm. H2O, the neural network approach yielded sensitivity and specificity of 71% and 69%, respectively. The AUC of the network was 0.75 (standard error = 0.01), A linear regression model produced identical results. Conclusions: This study shows that at an individual level, the outcome of PFS cannot be predicted accurately by the available non-invasive tests. The use of ANNs, which are better able than traditional regression models to identify non-linear relations and complex interactions between variables, did not improve the prediction of BOG. Thus, if precise urodynamic information is considered important in the diagnosis of men with LUTS, PFS must be carried out. Both neural networks and regression analysis appear promising to identify patients who should undergo PFS, and those in whom PFS can safely be omitted. Furthermore, the ability of ANNs and regression models to predict treatment result; should be evaluated.