Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual-orienting task with gaze-related or non-gaze-related directional cues, which triggered participants’ gaze-following behavior. Based on their eye-movement data registered by an eye tracker, we applied the machine learning algorithms to classify high-functioning children with ASD (HFA), low-functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye-tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that “less-social” directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual-orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD.

中文翻译：

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使用计算机视觉定向任务对自闭症谱系障碍儿童进行自动分类

自闭症谱系障碍 (ASD) 的早期筛查和诊断主要依赖于合格临床医生的行为观察，他们的决策过程可以从机器学习算法和传感器数据的组合中受益。我们设计了一个带有凝视相关或非凝视相关方向线索的计算机视觉定向任务，它触发了参与者的凝视跟随行为。根据眼动仪记录的眼动数据，我们应用机器学习算法对高功能 ASD 儿童 (HFA)、低功能 ASD 儿童 (LFA) 和正常发育儿童 (TD) 进行分类。我们发现TD儿童获得奖励的成功率高于HFA儿童，HFA儿童获得奖励的成功率高于LFA儿童。基于原始眼动追踪数据，我们的机器学习算法可以以 81.1% 的准确率和相对较高的灵敏度和特异性对三组进行分类。如果仅使用来自凝视或非凝视条件的数据，分类会变得更糟，这表明“较少社交”的方向线索也带有区分这些群体的有用信息。我们的研究结果不仅提供了关于 ASD 儿童视觉定向缺陷的见解，而且还证明了将经典行为范式与机器学习算法相结合以帮助筛查 ASD 个体的前景。表明“较少社交”的方向线索也带有区分这些群体的有用信息。我们的研究结果不仅提供了关于 ASD 儿童视觉定向缺陷的见解，而且还证明了将经典行为范式与机器学习算法相结合以帮助筛查 ASD 个体的前景。表明“较少社交”的方向线索也带有区分这些群体的有用信息。我们的研究结果不仅提供了关于 ASD 儿童视觉定向缺陷的见解，而且还证明了将经典行为范式与机器学习算法相结合以帮助筛查 ASD 个体的前景。