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: 10011854
• 负责人: Sally Ozonoff
• 金额: $ 71.09万
• 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/10011854
• 关键词: 4 year old; Affect; Age; Age-Months; Algorithms; Archives; Artificial Intelligence; 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; Modeling; 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; 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.

自闭症谱系障碍(ASD)的迹象在许多儿童出生后的第一年就出现了，但诊断是 通常制作的时间要晚得多，在美国平均年龄为4岁。早期干预是非常重要的 对患有自闭症的幼儿有效，但通常保留给有正式诊断的儿童，使其 尽早准确识别势在必行。一种筛查工具，可以在以下时间识别ASD风险 婴儿期提供了在全套症状出现之前进行干预的机会。在此应用程序中，我们 提出了两种新的基于视频的ASD检测方法。首先，我们将验证一个 最近开发的工具，视频参考婴儿自闭症评分系统(Virsa)，总的来说 社区婴儿样本。Virsa是一个简短的基于网络的乐器，它使用视频描述，而不是 而不是对行为的书面描述来检测ASD的迹象。它已经利用了数千小时的 通过之前的NIH资金收集和手工编码的视频。视频演示了一系列 行为和发展能力被呈现给父母，他们确定最具代表性的行为和发展能力 他们的孩子。通过之前的资助，我们已经确定VERSA具有良好的心理测量学特性 由有自闭症经验的父母使用时(例如，有较大的患病儿童)并表现出 它能够区分出婴儿在生命的第一年发展为自闭症。在目标1中，我们将研究 对ASD天真、没有家族病史的父母使用MEASURE。在目标2中，我们建议 另一种利用视频进行ASD检测的创新方法。机器学习是人工智能的一种应用 计算机程序根据训练数据进行自我学习和自我调整的智能 它们被公开，提高了性能，并在不显式的情况下对新数据进行泛化 程序化了。我们建议使用Virsa的视频，这是我们在初始验证中演示的 研究对ASD的早期症状敏感，作为训练输入来开发机器学习算法 自动检测ASD相关行为。为这个项目提供的巨大的视频档案，以及手- 编码的时间戳行为标签是机器学习的一种非常有价值的资源。目标2将奠定 为未来尝试开发用于ASD识别的基于视频的移动应用程序奠定基础，这需要 经过验证的分类器，可以识别对ASD早期检测至关重要的行为事件。终极目标 拟议项目的两个目标之一是开发低成本、低负担的措施，利用新的 包括移动平台、视频和机器学习方法在内的技术，以检测婴儿ASD风险。 这种措施将具有重大的公共卫生应用，包括筛查以社区为基础的大型 抽样和纵向跟踪儿科环境中的发展，以确定需要评估的儿童。 在婴儿期发现ASD将在大脑最具可塑性的最佳年龄进行治疗， 这可能会减少残疾，并可能防止出现后来出现的症状。