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

ASD筛查领域正处于十字路口：最受欢迎的筛查工具的灵敏度仅为33%- 38%1，3，儿科医生始终只将约三分之一的筛查工具失败的儿童转诊至 评估4，5 -引用缺乏信心的筛选结果作为主要原因，不可避免的5。在这 在这种背景下，ASD诊断和最终治疗的平均年龄保持在52岁左右并不完全令人惊讶 6个月-超过疾病的产前起源7年，超过可以可靠诊断的年龄 在许多情况下8.显然，新的方法需要测试。眼动追踪，它能产生生物相关的 社会和非社会视觉注意力模式的客观和可量化的指标，是一种技术， 作为一种工具，它很有希望极大地改变筛查的实施方式。在NIH的帮助下， 我们开发了6种新颖的眼动追踪测试，这些测试针对ASD儿童的关键挑战领域，包括 视觉社会注意力、目光转移和听觉社会注意力。利用我们的大型传统眼动追踪 数据集收集自跨越多个诊断组的> 2，000名幼儿，包括ASD，非ASD延迟， TD我们确定了每个测试的最佳眼动跟踪指标和截止值， 特异性和PPV（~97%和~90%），但灵敏度适中（每次测试~20%）。所有6项测试的组合 然而，显着提高灵敏度（~90%），并导致高分类准确性（AUC .95）。这些 然而，研究结果是在实验室环境中得到证实的，在现实世界的临床环境中的实用性未知。 因此，在AIM 1中，我们大胆地测试了眼动追踪是否在8,000人中进行12，18和24 一个月的婴儿健康检查（来自约5,2000名独特的幼儿），服务于来自各种种族，民族， SES背景检查在儿科诊所由医务人员实施时可以改善ASD早期筛查。 使用resarcher定义的标准进行眼动追踪失败的幼儿，以及通过的百分比，将通过以下方式进行评估： 有执照的心理学家对眼动追踪分数和眼动追踪的诊断分类准确性视而不见 计算。还将检查眼动追踪轮廓和临床表型之间的关系。为了 充分了解眼动追踪作为筛查工具的准确性，整个筛查的诊断结果 需要确定队列。因此，在AIM 2中，电子健康记录（EHR）将被利用，使我们能够 不仅确定用于检测ASD的眼跟踪的真实灵敏度、特异性、PPV和NPV，而且还 将结果与使用CSBS的ASD检测率进行比较，CSBS是一种父母报告筛选工具，用作标准， 作为我们Get SET Early模式的一部分9.最先进的生物信息学将使我们能够进一步 确定将眼动跟踪与父报告相结合是否上级单独使用任何一种方法。统计 建模将揭示筛选时的年龄、性别、种族、民族或SES等因素是否会影响眼睛 追踪分数最后，在AIM 3中，儿科医生和家长将对他们对眼动追踪的满意度进行评分。

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.