title: Prediction of conversion of laparoscopic cholecystectomy to open surgery with artificial neural networks.
abstract: BACKGROUND
  The intent of this study was to predict conversion of laparoscopic cholecystectomy (LC) to open surgery employing artificial neural networks (ANN).
  
  
  METHODS
  The retrospective data of 793 patients who underwent LC in a teaching university hospital from 1997 to 2004 was collected. We employed linear discrimination analysis and ANN models to examine the predictability of the conversion. The models were validated using prospective data of 100 patients who underwent LC at the same hospital.
  
  
  RESULTS
  The overall conversion rate was 9%. Conversion correlated with experience of surgeons, emergency LC, previous abdominal surgery, fever, leukocytosis, elevated bilirubin and alkaline phosphatase levels, and ultrasonographic detection of common bile duct stones. In the validation group, discriminant analysis formula diagnosed the conversion in 5 cases out of 9 (sensitivity: 56%; specificity: 82%); the ANN model diagnosed 6 cases (sensitivity: 67%; specificity: 99%).
  
  
  CONCLUSIONS
  The conversion of LC to open surgery is effectively predictable based on the preoperative health characteristics of patients using ANN.
authors.affiliation: Department of General Surgery, Sinaea Hospital, Tabriz University of Medical Sciences Tabriz, Iran. gholipour@TBZMED.ac.ir
authors.forename: Changiz
authors.initials: C
authors.lastname: Gholipour
authors.forename: Mohammad Bassir Abolghasemi
authors.initials: MB
authors.lastname: Fakhree
authors.forename: Rosita Alizadeh
authors.initials: RA
authors.lastname: Shalchi
authors.forename: Mehrshad
authors.initials: M
authors.lastname: Abbasi
mesh_terms: D002763:Cholecystectomy / Q000379:methods*; D017081:Cholecystectomy, Laparoscopic / Q000379:methods*; D016002:Discriminant Analysis; D005260:Female; D042882:Gallstones / Q000601:surgery*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
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