iPhone app effective for screening toddlers with autism: Study

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iPhone app effective for screening toddlers with autism: Study
iPhone app effective for screening toddlers with autism: Study

New York:An iPhone app has shown to be effective and easy to use for screening young children with signs of autism, paving the way to broader, easier access to screening other neurodevelopmental disorders, according to researchers.

The “Autism & Beyond” app first administers caregiver consent forms and survey questions and then uses the phone’s “selfie” camera to collect videos of young children’s reactions while they watch movies designed to elicit autism risk behaviours, such as patterns of emotion and attention, on the device’s screen.

The videos of the child’s reactions are sent to the study’s servers, where automatic behavioural coding software tracks the movement of video landmarks on the child’s face and quantifies the child’s emotions and attention.

For example, in response to a short movie of bubbles floating across the screen, the video coding algorithm looks for movements of the face that would indicate joy.

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An app-based approach can reach into underserved areas better and make it much easier to track an individual child’s changes over time, said Guillermo Sapiro, professor at Duke University in North Carolina.

The app, described in journal npj Digital Medicine, can analyse emotional reactions in children as young as 18 months and without having to see a specialist in person, allowing for earlier diagnosis and treatment.

Current screening for autism in young children is done in clinical settings, rather than the child’s natural environment, and highly trained people are needed to both administer the test and analyse the results.

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But, “this technology has the potential to transform how we screen and monitor children’s development”, Sapiro said.

For the study, 1,756 families with children aged from one to six years participated.

Parents completed 5,618 surveys and uploaded 4,441 videos.

Usable data were collected on 88 per cent of the uploaded videos, demonstrating for the first time the feasibility of this type of tool for observing and coding behaviour in natural environments.