Application of the Artificial Intelligence Algorithm Model for Screening of Inborn Errors of Metabolism

Inborn errors of metabolism (IEMs) are strongly related to abnormal growth and development in newborns and can even result in death. In total, 94,648 newborns were enrolled for expanded newborn screening using tandem mass spectrometry (MS/MS) from 2016 to 2020 at the Neonatal Disease Screening Center of the Maternal and Child Health Hospital in Shaoyang City, China. A total of 23 confirmed cases were detected in our study with an incidence rate of 1:4,115. A total of 10 types of IEM were identified, and the most common IEMs were phenylalanine hydroxylase deficiency (PAHD; 1:15,775) and primary carnitine deficiency (PCD; 1:18,930). Mutations in phenylalanine hydroxylase (PAH) and SLC22A5 were the leading causes of IEMs. To evaluate the application effect of artificial intelligence (AI) in newborn screening, we used AI to retrospectively analyze the screening results and found that the false-positive rate could be decreased by more than 24.9% after using AI. Meanwhile, a missed case with neonatal intrahepatic cholestasis citrin deficiency (NICCD) was found, the infant had a normal citrulline level (31 μmol/L; cutoff value of 6–32 μmol/L), indicating that citrulline may not be the best biomarker of intrahepatic cholestasis citrin deficiency. Our results indicated that the use of AI in newborn screening could improve efficiency significantly. Hence, we propose a novel strategy that combines expanded neonatal IEM screening with AI to reduce the occurrence of false positives and false negatives.