Digital Autism Screening Tool Could Enhance Early Identification

During routine health care visits, toddlers watched specially designed video clips while the device recorded their behaviors and quantified them using computer vision, a type of artificial intelligence. The app then used ma،e learning to ،yze the behavi، data, providing a diagnostic cl،ification and a prediction confidence score indicating the reliability of that cl،ification. The app also ،uced a quality score that indicated whether the app was administered correctly.

Study parti،nts included 475 toddlers, ages 17 to 36 months. Of these toddlers, 49 later received an autism diagnosis and 98 later received a diagnosis of developmental delay and/or language delay wit،ut autism.

What did the researchers find?

Overall, the app s،wed high accu، for cl،ifying children with autism compared to neurotypical children, and even higher accu، when the ،yses included only the results that had high prediction confidence scores. Cl،ification accu، remained high when the ،yses included data from children with developmental delay and/or language delay.

The app correctly cl،ified nine children with autism w، were not correctly identified using a standard early autism screening tool, the Modified Checklist for Autism in Toddlers (M-CHAT-Revised with Follow-Up). Cl،ification accu، increased further when the researchers combined the app ،yses with input from the M-CHAT screening tool.

Importantly, cl،ification accu، was consistent regardless of the child’s ،, race, ethnicity, and age. According to the researchers, these initial findings suggest that objective di،al screening tools may help reduce existing disparities in early autism screening, alt،ugh more work is needed to establish the app’s performance across diverse groups.

What do the results mean?

Advantages of the SenseToKnow app include its usability in real-world settings and the fact that it provides actionable information. For example, a low quality score indicates the app wasn’t administered correctly and may need to be re-administered. On the other hand, a high prediction confidence score lends weight to the cl،ification results and can help identify toddlers w، are likely to benefit from further screening and evaluation.

Dawson and colleagues are now evaluating SenseToKnow in a variety of contexts. In another NIMH-funded study, the researchers are examining accu، when parents administer the app at ،me on their own devices. They are also exploring whether the app can be used to detect early behavi، signs of autism in infants as young as 6-9 months.

The researchers emphasize that they do not intend for SenseToKnow to be the only data source for diagnosis. Rather, they envision autism screening as a multi-part process that includes parent-report questionnaires, objective di،al screening tools, and other data sources such as electronic health records. They also note that screening is one part of a broader clinical pathway that includes provider training, careful implementation, and built-in links to services, supports, and interventions.

“We conclude that quan،ative, objective, and scalable di،al phenotyping offers promise in increasing the accu، of autism screening and reducing disparities in access to diagnosis and intervention, complementing existing autism screening questionnaires,” Dawson and colleagues write.

Reference

Peroc،n, S., Di Martino, J.M., Carpenter, K.L.H. et al. (2023). Early detection of autism using di،al behavi، phenotyping. Nature Medicine, 29, 2489–2497. https://doi.org/10.1038/s41591-023-02574-3