Innovative Solutions Aim to Improve Screening and Diagnosis Reduce Healthcare Disparities

Early identification of autism is key, but the journey to diagnosis is not equal for everyone.

For many families, the road from initial concern to evaluation to autism diagnosis can be confusing, slow, and riddled with delays. Approved screening tools, although effective, are subjective measures, partially reliant upon caregiver questionnaires that have been shown to underperform with people of color, those with literacy barriers, and those who have lower educational backgrounds. Once a referral is made, a trained child development expert conducts a diagnostic assessment relying on observations of the child’s behavior during semi-standardized behavioral tasks. Access to licensed diagnosticians can be extremely limited, and in many rural and low-resource areas—including in the United States—there are no licensed providers. Duke Center for Autism and Brain Development investigators are working to reduce these disparities with digital, accessible, and userfriendly autism detection tools that improve screening techniques, open doors to access in low-resource communities, and help doctors track children’s progress.

“Today the playing field is not level. Those who gain easier access to screening tools and clinical diagnosis get the jump on evidence-based interventions and the benefits they bring,” said Duke Center for Autism Director Geraldine Dawson, Ph.D. “With creative, cross-disciplinary research, we can find innovative solutions that bring about equal access and reduce healthcare disparities.”

Infants on the autism spectrum tend to pay more attention to the world of objects. During the infant-toddler stage, they show more difficulty sustaining attention and their “gaze duration” differs with various visual stimuli.

Tracking Eye-gaze in Toddlers

"One of the first indications of autism is that an infant does not pay attention to the social world," Dawson notes. “How young children visually engage with others in social settings affects how brain circuits responsible for social interactions develop. When these circuits don’t develop as they should, it can lead to increased challenges for successful social engagement and communication later on.”

Infants on the autism spectrum tend to pay more attention to the world of objects. During the infant-toddler stage, they show more difficulty sustaining attention, and their “gaze duration” differs with various visual stimuli. Leveraging this knowledge, a transdisciplinary team of scientists including Dawson, Guillermo Sapiro, Ph.D., James B. Duke Distinguished Professor of Electrical and Computer Engineering, and Zhuoqing Chang, Ph.D., postdoctoral associate in Duke University’s Pratt School of Engineering, developed a digital app that successfully detects this telltale characteristic of autism in young children and could one day become an inexpensive, scalable early screening tool. In a study published in the Journal of the American Medical Association Pediatrics, the team showed that the app successfully measured whether a young child is more interested in looking at people or objects. The innovative, user-friendly app assesses the eye-gaze patterns of children as they watch short movies on an iPhone or iPad, then applies computer vision analysis and machine learning technology to determine what the child is paying attention to. The researchers worked with clinicians in pediatric primary care clinics to make sure the research is applicable to real world settings, such as an exam room, not just controlled lab environments.

In a sample clip from one of the digital app’s strategically designed movies, a man blows bubbles in the same frame that shows an inanimate object. The Duke University research team created the app to assess the eye-gaze patterns of children while they watched short movies on an iPhone or iPad, then applied computer vision analysis and machine learning to determine whether the child was looking more often at the person in the video, or objects.

Eye tracking has been used previously to assess gaze patterns in children. However, it has required special equipment and expertise to analyze the gaze patterns. This app, which takes less than 10 minutes to administer and uses the front-facing camera to record the child’s behavior, only requires an iPhone or iPad, making it readily accessible to primary care clinics in rural or economically disadvantaged communities and useable in home settings.

It Makes Sense to Know about Your Child

The eye-gaze tracking app is at the heart of the center’s Duke Sense to Know (S2K) Study, funded by the National Institutes of Mental Health, and currently underway. For the multi-year study, a multidisciplinary team is investigating the appbased assessment’s ability to identify differences in infants who are later diagnosed as being on the autism spectrum or as having neurodevelopmental conditions. In this longitudinal study, hundreds of infants are using the app, which records the baby’s behaviors, such as attention span, motor skills, emotional expressiveness, vocalizing, and interest in social cues. Using machine learning techniques, the team’s computer scientists analyze the videos of the child’s behavior to understand and compare eye-gaze and behavioral patterns.

“We hope that this technology will eventually provide greater access to autism screening, which is an essential first step to intervention. Our long-term goal is to create a well-validated, easy-to-use app that providers and caregivers can download and use, either in a regular clinic or home setting,” Dawson said. “We have additional steps to take, but our research suggests it might one day be possible.”

Chang Z, Di Martino JM, Aiello R, Baker J, Carpenter K, Compton S, Davis N, Eichner B, Espinosa S, Flowers J, Franz L, Harris A, Howard J, Perochon S, Perrin EM, Krishnappa Babu PR, Spanos M, Sullivan C, Walter BK, Kollins SH, Dawson G, Sapiro G. Computational methods to measure patterns of gaze in toddlers with autism spectrum disorder. JAMA Pediatr. 2021 Aug 1;175(8):827-836.