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

摘要-项目1 杜克自闭症卓越中心的总体目标是使用一个转化的数字健康和计算 方法，以解决更有效的自闭症筛查工具，客观的结果措施， 以及可用于自闭症幼儿临床试验的基于大脑的生物标志物。项目1的目标 评估基于计算机视觉分析和机器学习的新型数字行为评估工具 可以在现实世界中实现，以提高自闭症筛查的准确性， 对儿童的行为和发展进行客观的纵向监测。项目1将招募大量人口 16至30个月大的幼儿通过初级保健诊所，以评估远程管理的准确性 新的数字表型应用程序（APP）用于检测自闭症的早期迹象。应用程序会自动量化 使用计算机视觉分析直接观察儿童的行为，并部署在智能手机上， 平板电脑.我们将评估的敏感性，特异性，阴性/阳性预测值，和重测信度 由父母在家中提供的自闭症检测数字表型应用程序。我们亦会评估 应用程序在16-30、36和48个月大的自闭症儿童纵向结果监测中的可用性， 检查其收敛效度相比，标准化的临床措施。目标是扩大 类型的行为措施，可用于自闭症筛查和结果监测，我们将探讨 使用计算机视觉分析来测量从视频录制的亲子互动的可行性， 回家最后，与项目2合作，我们将设计一个自动化的临床决策支持， 护理提供者将自闭症筛查信息与有关转诊的可操作指导相结合， 诊断和服务，并评估其感知的可用性。我们的长期愿景是改变临床护理 通过提供创新的解决方案来解决获得护理方面的长期障碍。

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.