Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening

In medical applications, artificial intelligence (AI) methods have achieved considerable progress in various areas and also in newborn screening programs. In particular, interpretable AI methods have been applied in newborn screening aiming to increase analytical specificity and predictive power of screening results. In this study, we apply ensemble and deep learning methods in newborn screening for isovaleric aciduria (IVA) on a data set containing more than 2 million newborns. We show that these methods can reduce the number of newborns falsely classified with IVA by 100% with Extreme Gradient Boosting (XGBoost), by 78.94% with Random Forest (RF), and by 78.94% with Feed Forward Neural Networks (FFNN) compared to currently applied newborn screening methods. Furthermore, we show how explainable AI (XAI) methods can be used to interpret these black-box classification results and further apply them for potential biomarker discovery. The XAI methods reveal that besides the biomarker isovaleryl carnitine (C5), the birth year and the amino acid tryptophan (Trp) are influential in reducing the false positive rate. By this, we show that ensemble and deep learning could be highly beneficial in newborn screening and could have a major impact on newborns and their families, as it reduces false positive screening results and guides new directions for future research in this field.