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.
项目总结
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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