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
基本信息
- 批准号:10023938
- 负责人:
- 金额:$ 22万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAdultAgeAge-MonthsBehaviorBehavioralCaregiversCaringChildChildhoodClassificationComputer Vision SystemsComputing MethodologiesDataDevelopmentDevelopmental ProcessDiagnosisDiagnosticDimensionsDiseaseEvaluationEventFaceFoundationsFutureGoalsGrainIndividualIndividual DifferencesInfantInterdisciplinary StudyLifeLow PrevalenceMachine LearningMeasurementMeasuresMethodsModelingMovementParentsPatternPhenotypePlayPredictive ValuePrimary Health CareProcessPublic HealthReportingReproducibility of ResultsResearchRestRiskScreening procedureSeveritiesSocial DevelopmentSocial InteractionSpecificitySymptomsTechnologyTestingTimeTrainingValidationVisitage groupanalysis pipelineautism spectrum disorderbasebehavior measurementbehavior rating scalecomputational pipelinesdigitalearly screeninghigh riskhigh risk infantinfancyinnovationinterdisciplinary approachlensmachine learning methodmembernovelprimary care settingscreeningsocialsupport vector machinetemporal measurementtoolyoung adult
项目摘要
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.
项目摘要
该R21解决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
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,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动作。
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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ROBERT Thomas SCHULTZ其他文献
ROBERT Thomas SCHULTZ的其他文献
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{{ truncateString('ROBERT Thomas SCHULTZ', 18)}}的其他基金
Predicting Autism and Social Functioning from Computer Vision Analyses of Motor Synchrony During Dyadic Interactions
通过计算机视觉对二元交互过程中运动同步的分析来预测自闭症和社交功能
- 批准号:
10057391 - 财政年份:2019
- 资助金额:
$ 22万 - 项目类别:
Predicting Autism and Social Functioning from Computer Vision Analyses of Motor Synchrony During Dyadic Interactions
通过计算机视觉对二元交互过程中运动同步的分析来预测自闭症和社交功能
- 批准号:
10540333 - 财政年份:2019
- 资助金额:
$ 22万 - 项目类别:
Predicting Autism and Social Functioning from Computer Vision Analyses of Motor Synchrony During Dyadic Interactions
通过计算机视觉对二元交互过程中运动同步的分析来预测自闭症和社交功能
- 批准号:
10308068 - 财政年份:2019
- 资助金额:
$ 22万 - 项目类别:
Testing the hyperspecificity hypothesis: a neural theory of autism
检验超特异性假说:自闭症的神经理论
- 批准号:
8514729 - 财政年份:2012
- 资助金额:
$ 22万 - 项目类别:
Testing the hyperspecificity hypothesis: a neural theory of autism
检验超特异性假说:自闭症的神经理论
- 批准号:
8359473 - 财政年份:2012
- 资助金额:
$ 22万 - 项目类别:
Developing a Community-Based ASD Research Registry
开发基于社区的 ASD 研究登记处
- 批准号:
7830900 - 财政年份:2009
- 资助金额:
$ 22万 - 项目类别:
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