The prediction of common bile duct stones using a neural network

Background: The role of preoperative ERCP and endoscopic sphincterotomy (ES ) in the diagnosis and treatment of suspected common bile duct stones (CBDS ) in the laparoscopic age is controversial. The preoperative diagnosis of C BDS by ERCP and the removal of CBDS by ES are advantageous because of techn ical difficulties in performing laparoscopic exploration of the common bile duct. Approximately 50% of preoperative ERCP examinations are normal, howe ver. The noninvasive diagnosis of CBDS has assumed new importance, but it h as proved to be an elusive goal. Neural networks are a form of artificial c omputer intelligence that have been used successfully to interpret ECGs and to diagnose myocardial infarcts. The purpose of this study was to determin e whether a neural network could be trained to predict CBDS accurately in p atients at high risk of having duct stones. Study Design: We trained a back-propagation neural network to predict the p resence of CBDS. Retrospective data from patients who had a cholecystectomy and either a preoperative ERCP or intraoperative cholangiogram were used t o build the network, and it was tested using unseen data. Results: One hundred forty patients were used to train the network, and 16 patients were used to test it. The trained network was able to predict CBDS in 100% of the patients in both the training and test sets. Conclusions: Screening of high-risk patients for CBDS by neural network ana lysis is highly accurate. This promising new, noninvasive, and inexpensive technique can potentially decrease the need for preoperative ERCP by 50%, b ut additional prospective evaluation is indicated. (J Am Cell Surg 1398;187 :584-590. (C) 1998 by the American College of Surgeons).