The International Prostate Symptom Score (IPSS) is used exclusively for evaluating patients with a prostatic condition and following various treatment modalities. This non-invasive tool is insufficient for final clinical diagnosis. As decision support, we applied artificial neural networks (NN) in the diagnosis of men with lower urinary tract symptoms and to compare its performance to that of a traditional linear regression (LR) model for evaluating prostatic obstruction. This was a prospective study with 331 qualified patients visiting outpatient clinic at the hospital between 2001 and 2003 received investigation, consisting of trans-abdominal sonography, serum prostate specific antigen measurement, assessment of urinary symptoms and quality of life by the IPSS, urinary flow rate estimated based on all available non-invasive diagnostic test results plus patient age. Each final diagnosis was made by two independent urologists. There were five different sets of results for test groups:IPSS-7(urinary symptoms in IPSS), Logi-7( urinary symptoms in IPSS by LR), Logi-8(IPSS by LR), NN-7(urinary symptoms in IPSS by NN), and NN-8(IPSS by NN). The NNs showed better predictive values concerning the outcome of final diagnosis. The artificial neural networks would be an acceptable and useful tool for the clinical decision in the prediction of prostatic obstruction.
Lu, Chih-Cheng; Lee, Yu-Jen; and Roan, Jin-Sheng, "DECISION SUPPORT WITH NEURAL NETWORKS: IN IMPROVING DIAGNOSIS OF BENIGN PROSTATIC OBSTRUCTION BY THE INTERNATIONAL PROSTATE SYMPTOM SCORE" (2016). PACIS 2016 Proceedings. 393.