Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery.

Among various expert systems (ES), Artificial Neural Network (ANN) has shown to be suitable for the diagnosis of concurrent common bile duct stones (CBDS) in patients undergoing elective cholecystectomy. However, their application in practice remains limited since the development of ANNs represents a slow process that requires additional expertize from potential users. The aim of this study was to propose an ES for automated development of ANNs and validate its performances on the problem of prediction of CBDS. Automated development of the ANN was achieved by applying the evolutionary assembling approach, which assumes optimal configuring of the ANN parameters by using Genetic algorithm. Automated selection of optimal features for the ANN training was performed using a Backward sequential feature selection algorithm. The assessment of the developed ANN included the evaluation of predictive ability and clinical utility. For these purposes, we collected data from 303 patients who underwent surgery in the period from 2008 to 2014. The results showed that the total bilirubin, alanine aminotransferase, common bile duct diameter, number of stones, size of the smallest calculus, biliary colic, acute cholecystitis and pancreatitis had the best prognostic value of CBDS. Compared to the alternative approaches, the ANN obtained by the proposed ES had better sensitivity and clinical utility, which are considered to be the most important for the particular problem. Besides the fact that it enabled the development of ANNs with better performances, the proposed ES significantly reduced the complexity of ANNs' development compared to previous studies that required manual selection of optimal features and/or ANN configuration. Therefore, it is concluded that the proposed ES represents a robust and user-friendly framework that, apart from the prediction of CBDS, could advance and simplify the application of ANNs for solving a wider range of problems.

Scoring System Development and Validation for Prediction Choledocholithiasis before Open Cholecystectomy.

INTRODUCTION: Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. OBJECTIVE: The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. METHODS: We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. RESULTS: In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. CONCLUSION: Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

Regression tree for choledocholithiasis prediction.

OBJECTIVES: The aim of this study was to develop and compare the predictive accuracy of classification and regression tree (CART) analysis with logistic regression (LR) for predicting common bile duct stones (CBDS) in patients subjected to laparoscopic cholecystectomy. PATIENTS AND METHODS: We prospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography, cystic duct diameter) data for 154 patients considered for elective laparoscopic cholecystectomy at the department of General Surgery at Gornji Milanovac from 2013 through 2014. Univariate and multivariate regression analyses were used to determine independent predictors of CBDS. The CART analysis was carried out using the predictors identified by LR analysis. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve, and clinical utility using decision curve analysis. RESULTS: The most decisive variable at the time of classification was the cystic duct diameter category, the alkaline phosphatase, and dangerous stones. The CART model was shown to have good discriminatory ability (93.9%). Accuracy was similar in both models, ranging from 92.9% in the CART model and 93.5% in the LR model. In decision curve analysis, the CART model outperformed the LR model. CONCLUSION: We developed a user-friendly risk model that can successfully predict the presence of choledocholithiasis in patients planned for elective cholecystectomy. However, before recommending its use in clinical practice, a larger and more complete database should be used to further clarify the differences between models in terms of prediction of the CBDS.