title: Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis.
abstract: BACKGROUND
  Selection of patients with the highest probability for therapeutic ERCP remains an important task in a clinical workup of patients with suspected choledocholithiasis (CDL).
  
  
  OBJECTIVE
  To determine whether an artificial neural network (ANN) model can improve the accuracy of selecting patients with a high probability of undergoing therapeutic ERCP among those with strong clinical suspicion of CDL and to compare it with our previously reported prediction model.
  
  
  METHODS
  Prospective, observational study.
  
  
  METHODS
  Single, tertiary-care endoscopy center.
  
  
  METHODS
  Between January 2010 and September 2012, we prospectively recruited 291 consecutive patients who underwent ERCP after being referred to our center with firm suspicion for CDL.
  
  
  METHODS
  Predictive scores for CDL based on a multivariate logistic regression model and ANN model.
  
  
  METHODS
  The presence of common bile duct stones confirmed by ERCP.
  
  
  RESULTS
  There were 80.4% of patients with positive findings on ERCP. The area under the receiver-operating characteristic curve for our previously established multivariate logistic regression model was 0.787 (95% CI, 0.720-0.854; P < .001), whereas area under the curve for the ANN model was 0.884 (95% CI, 0.831-0.938; P < .001). The ANN model correctly classified 92.3% of patients with positive findings on ERCP and 69.6% patients with negative findings on ERCP.
  
  
  CONCLUSIONS
  Only those variables believed to be related to the outcome of interest were included. The majority of patients in our sample had positive findings on ERCP.
  
  
  CONCLUSIONS
  An ANN model has better discriminant ability and accuracy than a multivariate logistic regression model in selecting patients for therapeutic ERCP.
authors.affiliation: Department of Gastroenterology, University Clinical Center Tuzla, Tuzla, Bosnia and Herzegovina.
authors.forename: Predrag
authors.initials: P
authors.lastname: Jovanovic
authors.affiliation: Department of Gastroenterology, University Clinical Center Tuzla, Tuzla, Bosnia and Herzegovina.
authors.forename: Nermin N
authors.initials: NN
authors.lastname: Salkic
authors.affiliation: Department of Gastroenterology, University Clinical Center Tuzla, Tuzla, Bosnia and Herzegovina.
authors.forename: Enver
authors.initials: E
authors.lastname: Zerem
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002760:Cholangiopancreatography, Endoscopic Retrograde; D042883:Choledocholithiasis / Q000175:diagnosis* / Q000209:etiology / Q000628:therapy*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D018579:Patient Selection; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D055815:Young Adult