Early detection of autism using digital behavioral phenotyping.

Researchers tested a tablet app that screens for autism during regular doctor visits with 475 toddlers. The app shows activities that help identify autism signs using computer analysis. It correctly identified 88% of children with autism and had similar accuracy across boys and girls of different backgrounds. While it's very good at ruling out autism when results are negative (98% accurate), only 41% of positive results actually had autism, meaning some children would need further testing.

This prospective multiclinic study evaluated a digital autism screening application in 475 children aged 17-36 months during routine pediatric visits. The app used computer vision and machine learning to analyze behavioral responses to stimuli that elicit autism-related behaviors. Among participants, 49 were diagnosed with autism and 98 with developmental delay without autism. The digital screening algorithm achieved strong diagnostic accuracy (AUC=0.90) with 87.8% sensitivity and 80.8% specificity.

Notably, the algorithm maintained consistent sensitivity across sex, race, and ethnicity subgroups, addressing known disparities in traditional screening methods. The positive predictive value was 40.6%, while negative predictive value reached 97.8%, suggesting excellent ability to rule out autism when negative.

Digital phenotyping shows promise as an objective, scalable autism screening tool that could reduce diagnostic disparities. However, positive results require follow-up evaluation due to moderate positive predictive value. Integration with traditional questionnaires may enhance overall screening accuracy in primary care settings.

Positive predictive value of only 40.6% means many children with positive screens would not have autism, requiring additional evaluation. Study conducted in controlled research settings may not fully reflect real-world implementation challenges. Long-term outcomes and cost-effectiveness not assessed.

Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning.

An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.