Halil Arici, Marie Causey, Soma Patra, Uwe Kruger, Cristopher Antonio Villegas Uribe, Raun Melmed, Craig Ciuk, Sophia Crisler, Sarah Marler, Allyson Witters-Cundiff, Sanjeev Bhadresa, John Slattery, Juergen Hahn
While these results need to be replicated in a larger study, especially involving more children with non-ASD-related developmental delays, this work uses physiological measurements, coupled with AI, to support ASD diagnoses in a clinically relevant setting. The clinical trial that was part of this work was registered on clinicaltrials.gov as NCT04672967 and was entitled the Metabolic Autism Prediction (MAP) Study. The study was IRB approved by the Biomedical Research Alliance of New York (BRANY) IRB on August 12, 2021 (approval number: A21-10-282-888).
PURPOSE: Several clinical trial studies have shown correlations between certain physiological measurements and an ASD diagnosis. Such findings, however, have generally not resulted in tangible progress toward practical translation due to a number of factors which this work seeks to address.
METHODS: This paper presents a double-blind case/control trial design in which metabolic profiles, collected at two developmental pediatric clinics, were collected from children on a diagnostic waitlist for the purpose of developing a blood-based test for ASD. Besides obtaining blood samples, the children underwent gold-standard clinical evaluations, including the Autism Diagnostic Observation Schedule (ADOS), Mullen Scales of Early Learning (MSEL), and Vineland Adaptive Behavior Scale (VABS). The analysis, together with a complete medical history and physical exam, allowed confirmation or ruling out of suspected ASD using DSM-5 criteria. The study was based on a cohort of 140 children between the ages of 18 and 60 months that were referred to a developmental pediatrician because of concerns in their development.
RESULTS: 114 of these children received an ASD diagnosis, while 26 were diagnosed with non-ASD-related developmental delays. Based on the measured metabolites, artificial intelligence-based classification algorithms allowed for an over 80% accuracy in predicting whether a sample came from a child diagnosed with ASD or not.
CONCLUSION: While these results need to be replicated in a larger study, especially involving more children with non-ASD-related developmental delays, this work uses physiological measurements, coupled with AI, to support ASD diagnoses in a clinically relevant setting. The clinical trial that was part of this work was registered on clinicaltrials.gov as NCT04672967 and was entitled the Metabolic Autism Prediction (MAP) Study. The study was IRB approved by the Biomedical Research Alliance of New York (BRANY) IRB on August 12, 2021 (approval number: A21-10-282-888).