The Use of Artificial Neural Networks for Differential Diagnosis between Vesicoureteral Reflux and Urinary Tract Infection in Children

Abstract

Vesicoureteral reflux (VUR) and urinary tract infection (UTI) are common problems in children. Our goal is to use different models for the clinical decision of differential diagnosis of VUR and UTI in children. Materials and Methods: This was a retrospective cross-sectional study with 611 pediatric patients enrolled. Detailed information for the patients was obtained from hospital records and patient files. Three models including different variables were evaluated via an artificial neural network for the differential diagnosis of VUR and recurrent UTI. Clinical findings were included in Model 1, clinical and laboratory findings were included in Model 2, and clinical, laboratory and detailed urinary ultrasonography (USG) findings were included in Model 3. A cross-validation technique was used to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it. Results: Of the 611 children, 425 (69.6%) had VUR and 186 (30.4%) had UTI. The sensitivity of Model 1 and Model 2 were 0.682 and 0.856, respectively. Also, Model 3 showed the best performance and highest sensitivity with 0.939 for differential diagnosis. Conclusion: Differential diagnosis between VUR and UTI in children can be predicted by using clinical, laboratory and USG variables via an Artificial Neural Network. Model 3, which included clinical, laboratory and USG variables together, showed the best performance and highest sensitivity.

Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study

“…The conversion of records into data was carried out by pediatric nephrologists in the research study team, and a database was created by the same team. Therefore, data collection and database have a consistent information [ 28 ]. The variables determined by the nephrologists for the study were presented in Table 1 .…”

Artificial neural networks as clinical decision support systems

“…The dataset contains 611 children out of which 425 are VUR and the remaining 186 are suffering with UTI. Results reveal that model 1 and 2 has a sensitivity of 0.682 and 0.852 while the results provided by model 3 having a sensitivity value of 0.939 for diagnosis of differential VUR and UTI 88 are the most optimal. Erogul et al, 89 on the other hand, have proposed an ANN‐based approach for the recognition of down syndrome in children.…”

“…It is observed that most of the children suffering from recurrent urinary tract infection (UTI) have been diagnosed with Vesicoureteral reflux (VUR). We find an interesting technique that employs and analyzes three different models for which different variables are evaluated using ANN for diagnosis of VUR and recurrent UTI 88 . In the case of model 1, only clinical findings are used while model 2 contained laboratory findings too.…”