Novel computer vision-based assessment of infant-caregiver synchrony as an early level II screening tool for autism

项目摘要 这个R21解决了对能够检测自闭症谱系的准确和可扩展的筛查工具的迫切需求 自闭症（ASD）在生命的第一年。该项目将试点一种创新的数字表型筛选 方法，该方法使用计算机视觉和机器学习来测量简单的婴儿护理人员的同步性 交互.同步性是指婴儿自发地和动态地协调他们的 及时与他们的照顾者沟通。这一关键和早期出现的发展过程可能提供 关于婴儿患ASD风险的独特而精确的信息，同时也为早期理解提供了一个透镜， 自闭症核心的社会互动差异意义：该项目代表了ASD的范式转变 筛选，超越行为评级量表，转向更适合捕捉微妙的方法 ASD的早期指标。护理人员评定量表缺乏检测体征所需的粒度和客观性 自闭症的症状在第一年慢慢地出现。方法：跨学科研究团队将 利用尖端技术，在5分钟内客观、精确地测量同步性， 婴儿与看护者的互动无标记计算机视觉将用于量化面部运动， 使用小型双向摄像机进行拍摄。婴儿与照顾者面部表情的二进同步性 然后将在整个交互过程中计算移动，作为自动化机器学习管道的一部分。 初步数据：我们在简短的谈话中评估了患有和没有ASD的年轻人的这种方法。 与研究人员的互动。在机器学习分析管道中，同步特征分类 诊断准确率为91%-显著优于评估相同视频的专家临床医生。相同的 一组同步特征显著预测ASD组的症状严重程度，这表明该方法 对于诊断分类和个体差异的维度预测都有效。重要的是 pipeline还以同样高的准确性对儿童的诊断进行了分类，证明了 各年龄组的结果。目标。该项目将这些基于计算机视觉的方法扩展到婴儿， 总体目标是评估其作为ASD II级筛选器的实用性。目标1将评估并发 我们的计算措施的有效性，通过评估他们的关系， 建立临床医生管理的早期ASD标志物评估。目标2将评估我们的 在12个月时，通过测试其预测未来的能力， ASD诊断特异性高。影响：R21将为新型计算机视觉提供初步验证- 在婴儿期进行ASD筛查。通过针对自然的婴儿-看护者互动的动态，这种方法 有可能识别出社会发展中断的早期迹象，甚至在典型的ASD症状之前。 出现。此外，这种基于快速交互的筛选器很容易适应常规儿科护理的背景， 作为一个二级筛选器，有望在通用筛选框架内部署。

PROJECT SUMMARY This R21 addresses a critical need for accurate and scalable screening tools able to detect autism spectrum disorder (ASD) within the first year of life. This project will pilot an innovative digital phenotyping screening method, which uses computer vision and machine learning to measure synchrony within simple infant-caregiver interactions. Synchrony refers to the tendency for infants to spontaneously and dynamically coordinate their behaviors with their caregivers in time. This critical and early-emerging developmental process may provide unique and precise information about an infant’s risk for ASD, while also offering a lens for understanding early social interaction differences at the core of ASD. Significance: This project represents a paradigm shift in ASD screening, moving beyond behavior rating scales toward methods that are better suited to capture the subtle early indicators of ASD. Caregiver rating scales lack the granularity and objectivity necessary for detecting signs of ASD that emerge slowly and subtly throughout the first year. Approach: The interdisciplinary study team will leverage cutting-edge technology to objectively and granularly measure synchrony within 5-minute, play-based infant-caregiver interactions. Markerless computer vision will be used to quantify facial movements, captured unobtrusively with small, bidirectional cameras. The dyadic synchrony among infants’ and caregivers’ facial movements will then be calculated throughout the interaction, as part of an automated machine learning pipeline. Preliminary Data: We evaluated this approach in young adults with and without ASD during brief conversational interactions with research staff members. In a machine learning analysis pipeline, synchrony features classified diagnosis with 91% accuracy - significantly better than expert clinicians assessing the same videos. The same set of synchrony features significantly predicted symptom severity in the ASD group, suggesting that this method is effective for both diagnostic classification and dimensional prediction of individual differences. Importantly, the pipeline also classified diagnosis in children with similarly high accuracy, demonstrating the reproducibility of results across age groups. Aims. This project extends these computer vision-based methods to infants, with the overarching goal of evaluating their utility as a Level II screener for ASD. Aim 1 will evaluate the concurrent validity of our computational measures of interactional synchrony by evaluating their relationships with an established clinician-administered assessment of early ASD markers. Aim 2 will assess the utility of our interactional synchrony measure as a Level II screening tool at 12 months, by testing its ability to predict future ASD diagnosis with high specificity. Impact: This R21 will provide initial validation for a novel, computer vision- based screener for ASD in infancy. By targeting the dynamics of natural infant-caregiver interactions, this method has the potential to identify very early signs of disrupted social development, even before classic ASD symptoms emerge. Moreover, this quick interaction-based screener would fit easily into the context of routine pediatric care, holding promise as a Level II screener deployable within a universal screening framework.