Artificial Neural Networks in Urology 2004

Abstract

At the First Meeting of the European Society for Oncological Urology (ESOU) in Vienna the value of artificial neural networks in urology (ANN) were discussed. An ANN is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from clinical data. The use of ANNs has gained increasing popularity for applications where description of the dependency between dependent and independent variables is either unknown or very complex. This learning technique can be roughly described as a universal algebraic function that will distinguish signal from noise directly from clinical data. The application of ANNs to complex relationships makes them highly attractive for the study of complexed medical decision making. Recent applications include diagnosis, staging and progression of prostate cancer, progression of benign prostate hyperplasia, and bladder cancer recurrence in Ta/T1 bladder cancers. Accuracy of ANNs are between 77–91%, 81–88% and 80–90% for prostate cancer diagnosis, staging, and prediction of prognosis after radical prostatectomy, respectively.