Eye-tracking techniques could revolutionize autism diagnosis in primary care settings, aiding earlier and more accurate identification of the condition. Currently, nearly 3% of children in the U.S. are diagnosed with autism, but the demand for evaluations outstrips the availability of specialists, leading to long waiting periods.
A collaborative team from Indiana University and Purdue University is pioneering a solution. Led by Rebecca McNally Keehn, Ph.D., they conducted a groundbreaking study published in JAMA Network Open. The research focused on using eye-tracking biomarkers to diagnose autism in young children aged 14-48 months across primary care clinics in Indiana.
Eye-tracking biomarkers measure social and nonsocial attention, offering objective indicators of diagnosis. By analyzing eye movements and pupil size while children watched videos, researchers achieved remarkable results. Combining primary care clinician diagnosis with eye-tracking metrics yielded a diagnostic model with 91% sensitivity and 87% specificity, significantly enhancing diagnostic accuracy.
This approach addresses critical delays in autism evaluations, empowering primary care clinicians with a comprehensive diagnostic toolkit. By bridging the gap between research and clinical practice, it promises to improve access to timely and accurate diagnoses, particularly in underserved communities.
The team’s future plans involve replicating and validating their diagnostic model on a larger scale using artificial intelligence. Subsequent clinical trials will evaluate the model’s effectiveness in real-time primary care evaluations, potentially revolutionizing autism diagnosis and intervention.
