Testing the accuracy of eye tracking as a screening tool for ASD in the general population

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The field of ASD screening is at a crossroads: the sensitivity of the most popular screening tool is only 33%- 38%1,3, and pediatricians consistently refer only about a third of children who fail a screening tool for an evaluation4,5 - citing a lack of confidence in screening results as the primary reason for non-referral5. Within this context, it is not entirely surprising that the mean age of ASD diagnosis and eventual treatment remains at ~52 months6 - years beyond the disorder’s prenatal origins7, and beyond the age when it can be reliably diagnosed in many cases8. Clearly, new approaches need to be tested. Eye-tracking, which generates biologically-relevant, objective, and quantifiable metrics of social and non-social visual attention patterns, is a technology that holds considerable promise as a tool to dramatically change how screening is implemented. With the help of NIH funding, we developed 6 novel eye tracking tests that tap into key challenge areas for children with ASD including visual social attention, gaze shifting, and auditory social attention. Leveraging our large legacy eye tracking dataset collected from >2,000 toddlers spanning multiple diagnostic groups including ASD, non-ASD delay, and TD we determined optimal eye tracking metrics and cut-off values across for each test that result in very high specificity and PPV (~97% & ~90%) but modest sensitivity (~20% per test). Combining across all 6-tests however, dramatically improves sensitivity (~90%) and results in high classification accuracy (AUC .95). These findings, however, were demonstrated in a laboratory setting with utility in real-world clinical settings unknown. Thus, in AIM 1, we take the bold step of testing whether eye-tracking administered across 8,000 12, 18, & 24 month well-baby check-ups (from ~5,2000 unique toddlers) serving families from a wide range of racial, ethnic, and SES backrounds can improve ASD early screening when implemented by medical staff in pediatric offices. Toddlers who fail eye-tracking using resarcher defined criteria, and a percentage who pass, will be evaluated by a licensed psychologist blind to eye tracking scores, and diagnostic classification accuracy of eye-tracking computed. Relationships between eye tracking profiles and clinical phenotype will also be examined. In order to fully understand the accuracy of eye tracking as a screening tool, diagnostic outcomes of the entire screened cohort needs to be determined. Thus, in AIM 2 electronic health records (EHRs) will be leveraged to allow us to not only determine the true sensitivity, specificity, PPV and NPV of eye tracking for detecting ASD, but to also compare results to rates of ASD detection using the CSBS, a parent report screening tool used as standard of care in San Diego as part of our Get SET Early model9. State of-the-art bioinformatics will allow us to further determine if combining eye-tracking with parent report is superior relative to either approach alone. Statistical modeling will reveal whether or not factors such as age at screening, sex, race, ethnicity or SES impacts eye tracking scores. Finally in AIM 3, pediatricians and parents will rate their satisfaction with eye tracking.