Novel video-based approaches for detection of autism risk in the first year of life

基于视频的新颖方法可检测生命第一年的自闭症风险

• 批准号: 10434011
• 负责人: Sally Ozonoff
• 金额: $ 71.75万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-09 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10434011
• 关键词: 4 year old; Affect; Age; Age-Months; Algorithms; Archives; Artificial Intelligence; Autism Diagnosis; Behavior; Behavioral; Brain; Child; Childhood; Code; Communities; Competence; Computing Methodologies; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early Intervention; Early identification; Early treatment; Evaluation; Event; Facial Expression; Family history of; Foundations; Funding; Future; General Population; Goals; Hand; Health Services Accessibility; Hour; Improve Access; Infant; Internet; Intervention; Learning; Life; Lifestyle-related condition; Machine Learning; Measures; Methods; Online Systems; Parents; Performance; Property; Psychometrics; Public Health Applications Research; Resources; Risk; Sampling; Screening procedure; Secure; Sensitivity and Specificity; Symptoms; System; Techniques; Testing; Time; Training; United States; United States National Institutes of Health; autism spectrum disorder; autistic children; automated algorithm; base; clinically relevant; computer program; cost; deep learning model; detection method; disability; disease classification; disorder risk; experience; family burden; gaze; high risk; improved; infancy; innovation; innovative technologies; instrument; machine learning algorithm; machine learning method; mobile application; mobile computing; new technology; novel; prevent; recruit; response; screening; validation studies; vocalization

Signs of autism spectrum disorder (ASD) emerge in the first year of life in many children, but diagnosis is typically made much later, at an average age of 4 years in the United States. Early intervention is highly effective for young children with ASD, but is typically reserved for children with a formal diagnosis, making accurate identification as early as possible imperative. A screening tool that could identify ASD risk during infancy offers the opportunity for intervention before the full set of symptoms is present. In this application, we propose two novel video-based methods of detecting ASD in the first year of life. First, we will validate a recently developed instrument, the Video-referenced Infant Rating System for Autism (VIRSA), in a general community sample of infants. The VIRSA is a brief web-based instrument that utilizes video depictions rather than written descriptions of behavior to detect signs of ASD. It leverages thousands of hours of already collected and hand-coded video obtained through previous NIH funding. Videos demonstrating a continuum of behaviors and developmental competence are presented to parents, who identify the ones most representative of their child. Through previous funding, we have established that the VIRSA has good psychometric properties when used by parents with previous experience of ASD (i.e., have an older affected child) and demonstrated that it is able to distinguish infants developing ASD in the first year of life. In Aim 1, we will examine the measure’s use by parents who are naïve to ASD, with no family history of the disorder. In Aim 2, we propose another innovative method of utilizing video for ASD detection. Machine learning is an application of artificial intelligence in which computer programs “learn” and adjust themselves in response to training data to which they are exposed, improving performance and generalization to novel data without being explicitly programmed. We propose to use the videos from the VIRSA, previously demonstrated in our initial validation study to be sensitive to early signs of ASD, as training inputs to develop machine-learning algorithms for automatic detection of ASD-related behaviors. The huge video archive available for this project, with hand- coded time-stamped behavioral tags, is a highly valuable resource for machine learning. Aim 2 will lay the foundation for future attempts to develop video-based mobile applications for ASD recognition, which require validated classifiers that can recognize behavioral events central to early detection of ASD. The ultimate goal of the two aims of the proposed project is to develop low-cost, low-burden measures that capitalize on new technologies, including mobile platforms, video, and machine learning methods, to detect ASD risk in infancy. Such measures would have significant public health applications, including screening large community-based samples and longitudinally tracking development in pediatric settings to identify children requiring evaluation. Identification of ASD in infancy would afford treatment at an optimal age, when the brain is most malleable, which could lessen disability and possibly prevent the emergence of later-appearing symptoms.

许多儿童生命的第一年出现了自闭症谱系障碍（ASD）的迹象，但诊断是 通常，在美国平均4岁的平均年龄。早期干预高度 对ASD的幼儿有效，但通常适用于有正式诊断的儿童 尽早准确识别。筛选工具，可以识别ASD风险 婴儿期在存在全套症状之前提供了干预的机会。在此应用程序中，我们 提出两种基于视频的新型方法在生命的第一年中检测ASD。首先，我们将验证 最近开发的仪器是自闭症的视频引用的婴儿评级系统（VIRSA） 社区婴儿样本。 VIRSA是一种基于网络的简短仪器，使用视频描述而不是 比对ASD的迹象的行为描述相比。它利用数千小时已经 通过以前的NIH资金收集和手工编码的视频。视频展示了连续的 行为和发展能力呈现给父母，他们确定了最具代表性的人 他们的孩子。通过以前的资金，我们已经确定VIRSA具有良好的心理测量特性 当父母使用以前的ASD经验（即有一个年龄较大的孩子）并证明时 它能够区分婴儿在生命的第一年发展ASD。在AIM 1中，我们将检查 对ASD幼稚的父母的使用，没有这种疾病的家族史。在AIM 2中，我们建议 使用视频进行ASD检测的另一种创新方法。机器学习是人造的应用 计算机程序“学习”并根据培训数据进行调整的情报 它们暴露了，改善了对新数据的性能和概括，而无需明确的数据 程序。我们建议使用VIRSA的视频，以前在我们的初始验证中证明 研究对ASD的早期迹象敏感，作为训练输入以开发机器学习算法 自动检测与ASD相关的行为。该项目可用于此项目的巨大视频档案 - 编码的时标行为标签，是机器学习的高度宝贵资源。 AIM 2将放置 未来尝试开发基于视频的ASD识别的移动应用程序的基础，这需要 经过验证的分类器可以识别ASD早期检测中心的行为事件。最终目标 拟议项目的两个目标中，有两个低成本，低负荷的措施利用了新的 技术，包括移动平台，视频和机器学习方法，以检测婴儿期ASD风险。 此类措施将具有重要的公共卫生应用程序，包括筛查大型社区 样本和纵向跟踪小儿环境中的发展，以识别需要评估的儿童。 当大脑最具延展性时，婴儿期对ASD的识别将在最佳的年龄中得到治疗， 这可以减轻残疾，并可能阻止以后出现的症状的出现。