Summary: Eye tracking biomarkers may improve the diagnosis of autism in children. Combining eye tracking with primary care assessments increased diagnostic accuracy to 91% sensitivity and 87% specificity.
This approach could reduce long wait times for autism assessments, enabling rapid interventions. The research marks an important step toward faster and more accurate autism diagnosis.
Highlights:
- Eye tracking biomarkers combined with primary care assessments improve the accuracy of autism diagnosis.
- This method achieved a sensitivity of 91% and a specificity of 87% in the diagnosis of autism.
- The study addresses long wait times for autism evaluations, promoting timely interventions.
Source: Indiana University
In the United States, nearly 3% of all children are diagnosed with autism, according to the Centers for Disease Control and Prevention. But a collaborative team of researchers from Indiana University and Purdue University is finding ways to get the right diagnosis sooner.
“The number of children requiring autism evaluations exceeds the capacity of specialists trained to provide this service,” said Rebecca McNally Keehn, PhD, assistant professor of pediatrics at the IU School of Medicine.
Children and their families currently wait a year or more to access assessments. This is a problem because children miss opportunities to intervene at the optimal time of impact.
McNally Keehn is the lead author of an article recently published in Open JAMA Network which describes the research team’s study on diagnosing autism using eye tracking biomarkers in primary care clinics in Indiana.
The team visited practices participating in the Indiana Early Autism Evaluation Hub system and conducted a blinded, research-level evaluation on 146 children ages 14 to 48 months.
“Diagnostic biomarkers are characteristics that provide a discrete and objective indication of diagnosis. Eye-tracking biomarkers that measure social and non-social attention and brain function have been shown to differentiate young children diagnosed with autism from those with other neurodevelopmental disorders,” McNally Keehn said.
“However, despite huge investments in the discovery of eye-tracking biomarkers, there has been a gap in translating eye-tracking biomarkers into clinical benefits.
To conduct the eye tracking, children in the study sat in a high chair or on a caregiver’s lap and watched videos on a computer screen, while researchers recorded their eye movements and height of their pupils.
When a primary care clinician’s diagnosis and diagnostic certainty were combined with eye-tracking biomarker measurements, the model’s sensitivity was 91% and specificity was 87%, meaning they established a more accurate autism diagnosis.
McNally Keehn said studies like these can help reduce delays in accessing autism evaluations by better equipping primary care clinicians with a multi-method diagnostic approach.
“This is a public health issue and our approach has the potential to significantly improve access to accurate and rapid diagnosis in local communities,” said McNally Keehn.
The team’s next step is to conduct a large-scale replication and validation study of their diagnostic model using artificial intelligence. Next, they hope to conduct a clinical trial studying the effectiveness of the diagnostic model in real-time primary care assessments.
Other authors of the study include Patrick Monahan, Brett Enneking, Tybytha Ryan and Nancy Swigonski of IU and Brandon Keehn of Purdue.
About this news on autism research and eye tracking
Author: Christine Griffiths
Source: Indiana University
Contact: Christina Griffiths – Indiana University
Picture: Image is credited to Neuroscience News
Original research: Free access.
“Eye Tracking Biomarkers and Autism Diagnosis in Primary Care” by Rebecca McNally Keehn et al. Open JAMA Network
Abstract
Eye Tracking Biomarkers and Autism Diagnosis in Primary Care
Importance
Finding effective and scalable solutions to address diagnostic delays and disparities in autism is a public health imperative. Approaches that integrate eye-tracking biomarkers into community-based, multi-level autism assessment models hold promise for addressing this issue.
Objective
To determine whether a battery of eye-tracking biomarkers can reliably differentiate young children with and without autism in a community reference sample collected during a clinical evaluation in the primary care setting and to evaluate whether the combination of eye-tracking biomarkers with a primary care practitioner (PCP) diagnosis and diagnostic certainty are associated with diagnostic outcome.
Design, setting and participants
PCPs from the Autism Early Assessment (EAE) Hub system referred a consecutive sample of children to this prospective diagnostic study for blinded eye tracking index testing and follow-up assessment by 7 experts June 2019 to September 23, 2022. Participants included 146 children (aged 14 to 48 months) consecutively referred from 7 EAE hubs. Of 154 children enrolled, 146 provided usable data for at least one eye tracking measurement.
Main results and measures
The primary outcomes were the sensitivity and specificity of a composite eye tracking test (i.e. index), which was a consolidated measure based on significant eye tracking indices, compared to clinical diagnosis autism expert reference standard. Secondary outcome measures were sensitivity and specificity of an integrated approach using an index test and PCP diagnosis and certainty.
Results
Among 146 children (mean (SD) age, 2.6 (0.6) years; 104 (71%) male; 21 (14%) Hispanic or Latino and 96 (66%) non-Latino white; 102 (70 % with a reference standard diagnosis of autism), 113 (77%) had concordant autism results between the index (composite biomarker) and the reference results, with a sensitivity of 77.5% (CI 95 %, 68.4% – 84.5%) and a specificity of 77.3% (95% CI, 63.0% – 87.2%).
When the index diagnosis was based on the combination of a composite biomarker, PCP diagnosis, and diagnostic certainty, results were concordant with the reference standard for 114 of 127 cases (90%) with a sensitivity of 90.7 % (95% CI, 83.3%-95.0). %) and a specificity of 86.7% (95% CI, 70.3%-94.7%).
Conclusions and relevance
In this prospective diagnostic study, a composite eye-tracking biomarker was associated with a best-estimate clinical diagnosis of autism, and an integrated diagnostic model including PCP diagnosis and diagnostic certainty demonstrated sensitivity and specificity improved. These results suggest that equipping PCPs with a multi-method diagnostic approach has the potential to significantly improve access to accurate and rapid diagnosis in local communities.
News Source : neurosciencenews.com
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