ArticleFrontiers in pediatrics2022
Application of the Artificial Intelligence Algorithm Model for Screening of Inborn Errors of Metabolism.
Article in Frontiers in pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- From Genotype to Phenotype: Investigating SLC22A5 Variants and Their Significance in Carnitine Deficiency: A Systematic Review Study.Journal of cellular and molecular medicine · 2026Pooled it
- A Comprehensive Meta-Analytical Investigation into the Incidence of Neonatal Amino Acid Metabolic Disorders Across China.International journal of neonatal screening · 2026Review
- Untargeted metabolomics reveals distinct metabolic profiles in MUT-type methylmalonic acidemia.Molecular and cellular pediatrics · 2026Article
- Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.Metabolites · 2026Review
- Fully Automated Serum LC-MS/MS Platform and Pediatric Reference Intervals for Organic Acids, Amino Acids, and Acylcarnitines in Children (Ages 0-6 Years): Toward Quantitative Diagnosis of Inborn Errors of Metabolism.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Current Status of Newborn Bloodspot Screening Worldwide 2024: A Comprehensive Review of Recent Activities (2020-2023).International journal of neonatal screening · 2024Review
- Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.Turkish journal of medical sciences · 2024Article
- 206,977 newborn screening results reveal the ethnic differences in the spectrum of inborn errors of metabolism in Huaihua, China.Frontiers in genetics · 2024Article
- The significance of machine learning in neonatal screening for inherited metabolic diseases.Frontiers in pediatrics · 2024Article
- Newborn Screening of Primary Carnitine Deficiency: An Overview of Worldwide Practices and Pitfalls to Define an Algorithm before Expansion of Newborn Screening in France.International journal of neonatal screening · 2023Review
- Epidemiology of Hyperphenylalaninemia: A Systematic Review and Meta-Analysis.Sage open pediatricsReview
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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