![]() Sensitivity in this group ranged from 82-100%. The EEG algorithm was able to differentiate the ASD and low-risk/no-autism group with 100% specificity and positive predictive value (PPV) at all ages of testing. ![]() They found that the EEG algorithm was highly predictive in distinguishing those children who were ultimately diagnosed with ASD from those who were not, at as early as 3 months of age. The authors analyzed various features of the EEG data to create an algorithm to predict ASD diagnosis. Children were evaluated at several time points with EEG and the Autism Diagnostic Observation Schedule (ADOS), the current gold-standard clinical diagnostic tool which also quantifies severity of symptoms through the Calibrated Severity Score (CSS). Ultimately, 3 children from the low-risk group and 32 from the high-risk group were diagnosed with ASD. ![]() They enrolled 99 infant siblings of older children with a diagnosis of ASD ("high-risk" group), and 89 infants with no siblings or first-degree relatives with ASD ("low-risk" group) beginning at 3 months of age and continuing until 36 months. Investigators from Boston University and the University of San Francisco studied whether EEG could be reliably used as an early biomarker for diagnosis of autism spectrum disorder (ASD). ![]()
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