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.

许多儿童在出生后的第一年就会出现自闭症谱系障碍 (ASD) 的症状，但诊断需要 通常制作时间要晚得多，在美国平均制作时间为 4 岁。早期干预效果非常好 对患有自闭症谱系障碍 (ASD) 的幼儿有效，但通常保留给经过正式诊断的儿童，使得 尽早准确识别势在必行。一种可以识别 ASD 风险的筛查工具 婴儿期提供了在出现全套症状之前进行干预的机会。在这个应用程序中，我们 提出了两种基于视频的新颖方法来检测生命第一年的自闭症谱系障碍（ASD）。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级系统（VIRSA），一般来说 婴儿的社区样本。 VIRSA 是一种基于网络的简短工具，它利用视频描述而不是 而不是通过书面的行为描述来检测自闭症谱系障碍的迹象。它利用了数千个小时的时间 通过以前的 NIH 资助获得的收集和手工编码的视频。展示连续性的视频 行为和发展能力呈现给父母，他们找出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 VIRSA 具有良好的心理测量特性 当有自闭症谱系障碍经历的父母使用时（即，有一个较大的受影响的孩子）并证明 它能够区分出生第一年患有自闭症谱系障碍的婴儿。在目标 1 中，我们将检查 该措施适用于未患过自闭症谱系障碍 (ASD) 且没有该疾病家族史的父母。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应用 计算机程序“学习”并根据训练数据进行自我调整的智能 它们被暴露出来，从而提高了新数据的性能和泛化能力，而无需明确说明 程序。我们建议使用 VIRSA 的视频，之前在我们的初始验证中演示过 研究对自闭症谱系障碍的早期症状敏感，作为开发机器学习算法的训练输入 自动检测 ASD 相关行为。该项目有大量的视频档案可供使用， 编码的带时间戳的行为标签是机器学习的非常有价值的资源。目标 2 将奠定 为未来尝试开发基于视频的 ASD 识别移动应用程序奠定了基础，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。最终目标 拟议项目的两个目标之一是制定低成本、低负担的措施，利用新的 技术，包括移动平台、视频和机器学习方法，用于检测婴儿期自闭症谱系障碍风险。 此类措施将具有重要的公共卫生应用，包括筛查大型社区 样本并纵向跟踪儿科环境中的发育情况，以确定需要评估的儿童。 在婴儿期识别自闭症谱系障碍可以在最佳年龄进行治疗，因为此时大脑的可塑性最强， 这可以减少残疾并可能防止后来出现的症状的出现。