The Chronicle Duke researchers have discovered that autism spectrum disorder could be detected and treated faster through innovations in machine learning.
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Geraldine Dawson, director of the Duke Center for Autism and Brain Development, and Guillermo Sapiro, James B. Duke professor of electrical and computer engineering, have collaborated on a project to develop an app that captures toddlers’ responses to on-screen stimuli and analyzes the information for potential early signs of anxiety or autism spectrum disorder.
“We are unique in our approach, and to the best of our knowledge the first to do so,” Sapiro wrote in an email. “We have also collected the largest data, and we have also proved for the first time a number of concepts, from feasibility to new potential biomarkers.”
Although Dawson and Sapiro come from disparate fields of academia, their teams united to create a set of movies that elicit indicative responses such as facial expression and attention. Children can be shown the movies on devices like a tablet or phone, and the camera in the device tracks the child’s gaze and facial movements. Data from the devices is then analyzed and run through a set of algorithms developed by the team to estimate and quantify where the child is looking in order to assess the possible diagnosis of ASD.
In a video with Amazon Web Services, Dawson explains how the app can screen for ASD between 18 to 24 months of age, allowing for earlier detection and intervention, which can increase a child’s IQ by 17 points.
In 2015, an early, free version of the app—Autism & Beyond—was released for iOS-compatible devices. At the time, it wasn’t intended to be a diagnostic tool; rather, the researchers sought to check the reliability of facial expression video analysis and app-based questionnaires for screening for ASD and other development disorders.
“We are developing scalable methods of observing and measuring behavior automatically that can be delivered on common devices,” Dawson wrote in an email.
Dawson’s prior research centered on early detection and treatment of autism spectrum disorder through intervention on the developing brain. In the process, she demonstrated that symptoms of ASD could be screened for before an infant’s first birthday. Her laboratory studied differences in infantile brain activity associated with ASD and developed electrophysiological biomarkers that predict the severity of the disorder’s symptoms over time.
Along with Sally Rogers, professor of psychiatry and behavioral sciences at the University of California, Dawson used the information gleaned from her research to develop and validate the Early Start Denver Model, the first comprehensive behavioral therapy regimen for toddlers diagnosed with ASD.
In an article for JAMA Pediatrics, Dawson and Sapiro explained that typical screening for ASD has traditionally relied on reports from parents—at pediatric well-child visits, caregivers complete questionnaires based on the Diagnostic and Statistical Manual of Mental Disorders about their toddlers’ behavior. If responses indicate a certain number of symptoms, children are referred to a clinician for diagnostic assessment.[…]