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A digital health approach to early identification and outcome monitoring in autism

用于自闭症早期识别和结果监测的数字健康方法

基本信息

批准号：
10523407
负责人：
Geraldine Dawson
金额：
$ 4.34万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-07 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10523407
关键词：
Address Affect Age Age-Months Assessment tool Autism Diagnosis Automated Clinical Decision Support Behavior Behavior assessment Biological Markers Black race Caregivers Cellular Phone Child Child Behavior Child Development Clinic Clinical Clinical Trials Code Collaborations Computer Vision Systems Computers Computing Methodologies Detection Development Devices Diagnosis Diagnostic Services Early identification Education Engineering Ethnic Origin Facial Expression Family Goals Health Services Accessibility Hispanic Home Human Income Joints Knowledge Latino Machine Learning Measures Methods Monitor Motor Skills Musculoskeletal Equilibrium Names Nursery Schools Outcome Outcome Measure Parent-Child Relations Parents Participant Patient Recruitments Pattern Performance Phenotype Population Population Heterogeneity Predictive Value of Tests Primary Health Care Process Questionnaires Race Research Sampling Scientist Screening procedure Sensitivity and Specificity Standardization Tablets Testing Toddler Videotape Vision Work autism spectrum disorder autistic autistic children base behavior measurement behavioral outcome behavioral response biomarker discovery brain based care providers children of color clinical care clinical decision support design digital digital health follow-up girls health care settings implementation science improved innovation iterative design literacy movie novel pediatrician programs prototype recruit remote delivery response screening sex social attention standard of care tool usability

项目摘要

ABSTRACT – Project 1 The overall goal of the Duke Autism Center of Excellence is to use a translational digital health and computational approach to address the critical need for more effective autism screening tools, objective outcome measures, and brain-based biomarkers that can be used in clinical trials with young autistic children. The goal of Project 1 is to evaluate novel digital behavioral assessment tools based on computer vision analysis and machine learning that can be implemented in real-world settings to improve the accuracy of autism screening and enable scalable, objective longitudinal monitoring of children’s behavior and development. Project 1 will recruit a large population of 16- to 30-month-old toddlers through primary care clinics to evaluate the accuracy of a remotely administered novel digital phenotyping application (app) for detecting early signs of autism. The app automatically quantifies direct observations of children’s behavior using computer vision analysis and is deployed on a smartphone or tablet. We will assess the sensitivity, specificity, negative/positive predictive values, and test-retest reliability of the digital phenotyping app for autism detection when delivered by parents at home. We will also assess the app’s usability for longitudinal outcome monitoring of autistic children at 16-30, 36, and 48 months of age by examining its convergent validity compared to standardized clinical measures. With a goal of expanding the types of behavioral measures that could be used for autism screening and outcome monitoring, we will explore the feasibility of using computer vision analysis to measure parent-child interaction from videos recorded at home. Finally, in collaboration with Project 2, we will design an automated clinical decision support for primary care providers that integrates autism screening information with actionable guidance regarding referrals for diagnosis and services and assess its perceived usability. Our long-term vision is to transform how clinical care is delivered by providing innovative solutions that address long-standing barriers in access to care.

摘要-项目1