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

基于计算机视觉的婴儿-看护者同步性评估作为自闭症早期 II 级筛查工具

• 批准号: 10023938
• 负责人: ROBERT Thomas SCHULTZ
• 金额: $ 22万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-24 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10023938
• 关键词: Address; Adult; Age; Age-Months; Behavior; Behavioral; Caregivers; Caring; Child; Childhood; Classification; Computer Vision Systems; Computing Methodologies; Data; Development; Developmental Process; Diagnosis; Diagnostic; Dimensions; Disease; Evaluation; Event; Face; Foundations; Future; Goals; Grain; Individual; Individual Differences; Infant; Interdisciplinary Study; Life; Low Prevalence; Machine Learning; Measurement; Measures; Methods; Modeling; Movement; Parents; Pattern; Phenotype; Play; Predictive Value; Primary Health Care; Process; Public Health; Reporting; Reproducibility of Results; Research; Rest; Risk; Screening procedure; Severities; Social Development; Social Interaction; Specificity; Symptoms; Technology; Testing; Time; Training; Validation; Visit; age group; analysis pipeline; autism spectrum disorder; base; behavior measurement; behavior rating scale; computational pipelines; digital; early screening; high risk; high risk infant; infancy; innovation; interdisciplinary approach; lens; machine learning method; member; novel; primary care setting; screening; social; support vector machine; temporal measurement; tool; young adult

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.

项目概要 R21 满足了对能够检测自闭症谱系的准确且可扩展的筛查工具的迫切需求 生命第一年内患有自闭症谱系障碍（ASD）。该项目将试点创新的数字表型筛选 方法，使用计算机视觉和机器学习来测量简单婴儿护理人员的同步性 互动。同步性是指婴儿自发地、动态地协调他们的能力的倾向。 及时与他们的照顾者进行行为。这一关键且早期出现的发展过程可能会提供 有关婴儿患自闭症谱系障碍 (ASD) 风险的独特而准确的信息，同时还提供了早期了解的视角 社交互动差异是 ASD 的核心。意义：该项目代表了 ASD 的范式转变 筛选，超越行为评级尺度，转向更适合捕捉微妙之处的方法 ASD 的早期指标。护理人员评级量表缺乏检测迹象所需的粒度和客观性 自闭症谱系障碍（ASD）在第一年缓慢而微妙地出现。方法：跨学科研究小组将 利用尖端技术客观、细致地测量 5 分钟内基于游戏的同步性 婴儿与照顾者的互动。无标记计算机视觉将用于量化捕获的面部运动 使用小型双向相机不引人注意。婴儿和护理人员面部表情的二元同步 然后，作为自动化机器学习管道的一部分，将在整个交互过程中计算运动。 初步数据：我们在简短的对话中评估了患有和不患有自闭症谱系障碍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 诊断准确率高达 91% - 明显优于评估相同视频的专家临床医生。相同 一组同步特征显着预测了 ASD 组的症状严重程度，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 还以同样高的准确度对儿童进行了分类诊断，证明了诊断的可重复性 各个年龄段的结果。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 二级筛查的效用。目标 1 将评估并发 通过评估交互同步的关系来验证我们的交互同步计算测量的有效性 建立了临床医生对早期 ASD 标志物进行的评估。目标 2 将评估我们的效用 通过测试其预测未来的能力，将交互同步性测量作为 12 个月时的 II 级筛选工具 ASD诊断具有较高的特异性。影响：这款 R21 将为新颖的计算机视觉提供初步验证 - 基于婴儿期 ASD 的筛查。通过针对自然婴儿与护理人员互动的动态，该方法 甚至在典型的自闭症谱系障碍症状出现之前，就有可能识别出社会发展紊乱的早期迹象 出现。此外，这种基于交互的快速筛查仪可以轻松融入常规儿科护理环境中， 有望作为可在通用筛查框架内部署的二级筛查仪。