Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder.

Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD. The Device combines behavioral features from three distinct inputs (a caregiver questionnaire, analysis of two short home videos, and an HCP questionnaire) in a gradient boosted decision tree machine learning algorithm to produce either an ASD positive, ASD negative, or indeterminate output. This study compared Device outputs to diagnostic agreement by two or more independent specialists in a cohort of 18-72-month-olds with developmental delay concerns (425 study completers, 36% female, 29% ASD prevalence). Device output PPV for all study completers was 80.8% (95% confidence intervals (CI), 70.3%-88.8%) and NPV was 98.3% (90.6%-100%). For the 31.8% of participants who received a determinate output (ASD positive or negative) Device sensitivity was 98.4% (91.6%-100%) and specificity was 78.9% (67.6%-87.7%). The Device's indeterminate output acts as a risk control measure when inputs are insufficiently granular to make a determinate recommendation with confidence. If this risk control measure were removed, the sensitivity for all study completers would fall to 51.6% (63/122) (95% CI 42.4%, 60.8%), and specificity would fall to 18.5% (56/303) (95% CI 14.3%, 23.3%). Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants' sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources.

Test of variables of attention performance among ADHD children with credible vs. non-credible PVT performance.

The assessment of effort is a crucial step in the evaluation of children and adolescents who present with symptoms of an Attention-Deficit/Hyperactivity Disorder (ADHD). Studies with adults have found that a large percentage of individuals claiming to have ADHD fail performance validity measures. In children, failure on PVT measures is associated with lower scores on a wide array of neuropsychological measures. The current study examined the performance of 50 children diagnosed with ADHD on the basis of whether they passed (N = 25) versus failed (N = 25) a standalone PVT, on the Test of Variables of Attention (TOVA), the Wisconsin Card Sorting Test - 64 (WCST) and the Tower of London: Drexel (TOL). Subjects who failed one or more PVTs scored significantly below those who passed, on the Omission scores of the TOVA and on several dimensions of the WCST. No significant differences were found on the TOL scores. Specifically, subjects who failed PVTs scored more than two standard deviations below the mean on the first half TOVA Omission errors score, whereas those who passed PVTs scored within the Average range. It is proposed that first half Omission scores on the TOVA may represent an embedded measure of effort.