Eye tracking can help with early autism diagnosis, study finds

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In the United States, nearly 3% of all children are diagnosed with autism, as reported by the Centers for Disease Control and Prevention.

Addressing this growing concern, researchers from Indiana University and Purdue University are pioneering efforts to diagnose autism more quickly and accurately.

Rebecca McNally Keehn, Ph.D., an assistant professor of pediatrics at the IU School of Medicine, highlights a significant challenge: the demand for autism evaluations far exceeds the available specialist resources.

Families often wait over a year for these critical assessments, during which time children may miss out on early interventions that are most effective.

Dr. McNally Keehn is leading research efforts detailed in a study published in JAMA Network Open. The study focuses on the use of eye tracking as a tool for autism diagnosis in young children.

Her team visited primary care clinics participating in the Indiana Early Autism Evaluation Hub system. They carried out a detailed study on 146 children aged between 14 and 48 months.

The method they used involves tracking where and how long a child looks at videos displayed on a computer screen.

This process, known as eye tracking, measures both social and non-social attention and brain function. These measurements can help distinguish children with autism from those with other neurodevelopmental issues.

Despite significant advancements in identifying eye-tracking indicators (biomarkers) for autism, these findings have not been fully utilized in everyday clinical settings.

McNally Keehn’s study aims to bridge this gap by combining traditional diagnostic methods with these innovative biomarkers.

In practical terms, children participating in the study sat in a highchair or on their caregiver’s lap while watching specific videos. Researchers then recorded their eye movements and pupil sizes.

This data, when combined with assessments from primary care clinicians, enhanced the accuracy of autism diagnoses significantly. The study reported a 91% sensitivity and 87% specificity in their model, indicating a high level of accuracy.

Dr. McNally Keehn believes that incorporating this method into routine primary care can reduce the waiting times for diagnoses and ensure that children receive timely and precise evaluations.

Such advancements could represent a significant step forward in public health regarding autism care and management.

Building on these promising results, the next steps involve expanding the study. The team plans to replicate and validate their findings on a larger scale using artificial intelligence to refine their diagnostic model further.

Following this, a clinical trial will assess how effectively this model works in real-time during regular primary care visits.

This research not only promises to enhance diagnostic processes but also to empower primary care physicians with more reliable tools for identifying autism early.

The team, including IU’s Patrick Monahan, Brett Enneking, Tybytha Ryan, Nancy Swigonski, and Purdue’s Brandon Keehn, continues to explore how these technological advances can be integrated into everyday medical practice, thereby transforming the landscape of autism diagnosis.

If you care about autism, please read studies about a new cause of autism, and cats may help decrease anxiety for kids with autism.

For more information about health, please see recent studies about vitamin D that may hold the clue to more autism, and results showing strange eating habits may signal autism.

The research findings can be found in JAMA Network Open.

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