Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network
It addresses earlier diagnosis of FASD to improve children's quality of life, but it is incremental as it applies an existing method to a new medical dataset.
This study tackled the problem of detecting Fetal Alcohol Spectrum Disorder (FASD) in children by using artificial neural networks (ANNs) on psychometric, saccade eye movement, and diffusion tensor imaging data, achieving up to 88% accuracy in predictions.
Fetal alcohol spectrum disorder (FASD) is a syndrome whose only difference compared to other children's conditions is the mother's alcohol consumption during pregnancy. An earlier diagnosis of FASD improving the quality of life of children and adolescents. For this reason, this study focus on evaluating the use of the artificial neural network (ANN) to classify children with FASD and explore how accurate it is. ANN has been used to diagnose cancer, diabetes, and other diseases in the medical area, being a tool that presents good results. The data used is from a battery of tests from children for 5-18 years old (include tests of psychometric, saccade eye movement, and diffusion tensor imaging (DTI)). We study the different configurations of ANN with dense layers. The first one predicts 75\% of the outcome correctly for psychometric data. The others models include a feature layer, and we used it to predict FASD using every test individually. The models accurately predict over 70\% of the cases, and psychometric and memory guides predict over 88\% accuracy. The results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. However, we could be careful in used as a diagnostic technique.