ArticleChildren (Basel, Switzerland)2023
Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants.
Article in Children (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods.Frontiers in pediatrics · 2025Pooled it
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Point of Care Testing, Rapid Next Generation Sequencing and Artificial Intelligence in Pediatric and Neonatal Healthcare: A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- A Splicing Variant in RDH8 Is Associated with Autosomal Recessive Stargardt Macular Dystrophy.Genes · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Mendelian disorders are prevalent in neonatal and pediatric intensive care units and are a leading cause of morbidity and mortality in these settings. Current diagnostic pipelines that integrate phenotypic and genotypic data are expert-dependent and time-intensive. Artificial intelligence (AI) tools may help address these challenges. Dx29 is an open-source AI tool designed for use by clinicians. It analyzes the patient's phenotype and genotype to generate a ranked differential diagnosis. We used Dx29 to retrospectively analyze 25 acutely ill infants who had been diagnosed with a Mendelian disorder, using a targeted panel of ~5000 genes. For each case, a trio (proband and both parents) file containing gene variant information was analyzed, alongside patient phenotype, which was provided to Dx29 by three approaches: (1) AI extraction from medical records, (2) AI extraction with manual review/editing, and (3) manual entry. We then identified the rank of the correct diagnosis in Dx29's differential diagnosis. With these three approaches, Dx29 ranked the correct diagnosis in the top 10 in 92-96% of cases. These results suggest that non-expert use of Dx29's automated phenotyping and subsequent data analysis may compare favorably to standard workflows utilized by bioinformatics experts to analyze genomic data and diagnose Mendelian diseases.
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Registered trials
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