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Unlocking Autism Insights: The Revolutionary Role of Eye-Tracking Technology in Early Diagnosis

Eye-tracking technology is making a significant breakthrough in the early diagnosis of autism. A recent research study published in JAMA Network Open has shown that using eye-tracking biomarkers during primary care evaluations can increase diagnosis accuracy to 91% sensitivity and 87% specificity. This discovery is a remarkable stride towards faster and more accurate autism diagnosis, decreasing long waiting periods for evaluations and enabling timely interventions.

Groundbreaking Research

The study was conducted by a collaborative group of researchers from Indiana University and Purdue University. With nearly 3% of children in the United States diagnosed with autism, these researchers aimed to expedite diagnosis processes and enable more optimal intervention times.

Lead author and assistant professor of pediatrics at the IU School of Medicine, Rebecca McNally Keehn, has indicated the existence of a demand-supply gap as the number of children needing autism evaluations outstrips the capacity of available specialists. Hence the development of a novel approach that leverages eye-tracking biomarkers for diagnosis in primary care is deemed impactful.

Eye Tracking Biomarkers in Autism Diagnosis

In this research, scientists used eye-tracking biomarkers to record eye movements and pupil size as children, aged between 14 and 48 months, watched videos on a computer screen. These biomarkers provide a distinct and objective indication of autism, helping differentiate children with autism from others with neurodevelopmental disabilities.

Combining eye-tracking biomarker metrics with the primary care clinician’s diagnosis and diagnostic certainty yielded a significantly more accurate autism diagnosis. This multi-method diagnostic approach can be beneficial for primary care clinicians, facilitating a significant reduction in access delays to autism evaluations.

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Next Steps in Autism Diagnosis

The research team is looking to replicate and validate their diagnostic model on a larger scale, using artificial intelligence tools to increase its efficiency. Following this, they intend to begin a clinical trial to examine the model’s effectiveness in real-time primary care evaluations. The team includes Patrick Monahan, Brett Enneking, Tybytha Ryan, and Nancy Swigonski from IU, and Brandon Keehn from Purdue.

Conclusion

This innovative approach to autism diagnosis exemplifies the potential of integrating new technology into healthcare diagnoses. By leveraging eye-tracking biomarkers, healthcare professionals can increase the accuracy and speed of autism diagnosis, allowing for quicker and more effective interventions.

Original Research: Open access.“Eye-Tracking Biomarkers and Autism Diagnosis in Primary Care” by Rebecca McNally Keehn et al. JAMA Network Open.