• Research Highlights
A tablet-based screening tool that analyzes children’s behavior in response to specific video clips shows promise for improving autism screening, according to a study supported in part by the National Institute of Mental Health. While early autism screening typically depends on parent questionnaires, data suggest that the accuracy of these assessments may vary across settings and populations. Objective measurement tools, including digital technologies, could help improve real-world screening and reduce disparities in early screening and identification.
What did the researchers do?
In the study, researchers Geraldine Dawson, Ph.D. , Guillermo Sapiro, Ph.D. and colleagues at Duke Center for Autism and Brain Development try a tablet-based app called SenseToKnow. The app uses the tablet’s camera to record various child behaviors, including gaze patterns, facial expressions, head movements, blink rate, and whether the child responded to its name. According to the researchers, this multimodal approach allows them to capture the range of behavioral variations that children with autism can display.
During routine healthcare visits, the 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 machine learning to analyze the behavioral data, providing a diagnostic classification and a predictive confidence score indicating the reliability of that classification. The app also produced a quality score that showed whether the app was done right.
Study participants included 475 toddlers, ages 17 to 36 months. Of these infants, 49 later received a diagnosis of autism and 98 later a diagnosis of developmental delay and/or language delay without autism.
What did the researchers find?
Overall, the app showed high accuracy for classifying children with autism compared to neurotypical children, and even greater accuracy when analyzes included only the results that had high prediction confidence scores. Classification accuracy remained high when analyzes included data from children with developmental delay and/or language delay.
The app correctly classified nine children with autism who were misdiagnosed using a standard early autism screening tool, the Modified Checklist for Autism in Toddlers (M-CHAT-Revised with Follow-up). Classification accuracy was further increased when the researchers combined the app’s analyzes with data from the M-CHAT screening tool.
Importantly, classification accuracy was consistent regardless of child gender, race, ethnicity, and age. According to the researchers, these initial findings suggest that objective digital screening tools may help reduce existing disparities in early autism screening, although more work is needed to determine how the app performs in different 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 that the application was not managed properly and may need to be managed again. On the other hand, a high predictive confidence score adds weight to the classification results and may help identify infants who are likely to benefit from further screening and assessment.
Dawson and colleagues are now evaluating SenseToKnow in several contexts. In another NIMH-funded study, researchers are looking at accuracy when parents administer the app at home on their own devices. They are also investigating whether the app can be used to detect early behavioral signs of autism in infants aged 6-9 months.
The researchers emphasize that they do not intend for SenseToKnow to be the only source of data for diagnosis. Instead, they envision autism screening as a multifaceted process that includes parent-report questionnaires, objective digital 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 education, careful implementation, and integrated linkages to services, supports, and interventions.
“We conclude that quantitative, objective, and scaled digital phenotyping offers promise for increasing the accuracy of autism screening and reducing disparities in access to diagnosis and intervention by complementing existing autism screening questionnaires,” write Dawson and his associates.
Report
Perochon, S., Di Martino, JM, Carpenter, KLH et al. (2023). Early detection of autism using digital behavioral phenotyping. Nature Medicine, 292489–2497. https://doi.org/10.1038/s41591-023-02574-3
