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Open Access Highly Accessed Research article

Prediction of conversion of laparoscopic cholecystectomy to open surgery with artificial neural networks

Changiz Gholipour1*, Mohammad Bassir Abolghasemi Fakhree1, Rosita Alizadeh Shalchi2 and Mehrshad Abbasi3

Author Affiliations

1 Department of General Surgery, Sinaea Hospital, Tabriz University of Medical Sciences Tabriz, Iran

2 Department of Internal Medicine, Sinaea Hospital, Tabriz University of Medical Sciences Tabriz, Iran

3 Endocrine and Metabolism Research Center, Vali-asr Hospital, Tehran University of Medical Sciences, Tehran, Iran

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BMC Surgery 2009, 9:13  doi:10.1186/1471-2482-9-13

Published: 21 August 2009

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%).

Conclusion

The conversion of LC to open surgery is effectively predictable based on the preoperative health characteristics of patients using ANN.