Application Of Artificial Neural Network In The Prediction Of Spontaneous Ureteral Calculus Passage

Object:Ureteral stone is a multifactorial disease,which is common in urological practice and also a substantial public health issue.As a traditional treatment,medical expulsive therapy is widely accepted by patients.To establish the prediction model of the spontaneous ureteral calculus passage by applying artificial neural network and put it into clinical application.Methods:From January to August in 2013,we enrolled 225 patients with ureteral calculus who received treatment in the Department of Urology,the First Affiliated Hospital of Medical College,Shihezi University.After 4-week medical expulsive treatment,the status of calculus was examined by urinary tract ultrasound or CT,and patients were divided into two groups:calculus removed group and non calculus removed group.By univariate analysis,influencing factors for removing calculus were selected and were applied as predictive parameters in the establishment of artificial neural network prediction model,and the model was used to make prediction on 68 testing Samples.ROC curve was made to predict quasi-probability,and the AUC was calculated to predict efficacy.In order to further verify the validity and applicability of the model,we randomly chose 44 patients and applied the ANN model to prediction of spontaneous ureteral stone passage.Results:There were 141 patients in calculus removed group and 84 patients in non calculus removed group.Univariate analysis showed that the two groups were not significantly different in gender,BMI,bladder irritation symptoms,lesion side,hydronephrosis,urine pH value,hematuresis and lymphocyte count(P > 0.05);the two groups were significantly different in age,pain degree,calculus size,position of calculus,leucocyte count,neutrophil count,neutrophil percentage,lymphocyte percentage and C-reactive protein level(P < 0.05).Artificial neural network was operated with all together 9 neurons in the input layer.Two hidden layers were established in the automatic system,and there was one neuron in the output layer.The first three predictive variables in importance were calculus size(0.20),C-reactive protein level(0.18)and age(0.12).The neural network models that were successfully built were applied in the prediction of 68 testing samples,and the results showed that the sensitivity,specificity and total accuracy rate of artificial neural network model were93.3%,60.9% and 82.4% respectively,and the AUC was 0.868 [ 95% CI(0.774,0.962)].In the process of clinical application,the sensitivity,specificity and total accuracy rate of artificial neural network model were 100.00%,64.29% and 88.64% respectively.Conclusion:Artificial neural network model can accurately predict spontaneous ureteral calculus passage and can assist clinicians choose safe and reasonable treatment plan for ureteral calculus patients.