AUTHOR=Liu Jia , Dai ShuYang , Chen Gong , Sun Song , Jiang JingYing , Zheng Shan , Zheng YiJie , Dong Rui 

TITLE=Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

JOURNAL=Frontiers in Pediatrics

VOLUME=8

YEAR=2020

URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2020.00409

DOI=10.3389/fped.2020.00409

ISSN=2296-2360

ABSTRACT=<p><bold>Objectives:</bold> Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN).</p><p><bold>Methods:</bold> A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness.</p><p><bold>Results:</bold> We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%.</p><p><bold>Conclusions:</bold> The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.</p>