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: 10794112
• 负责人: Sally Ozonoff
• 金额: $ 15.85万
• 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 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794112
• 关键词: 4 year old; Address; Administrative Supplement; Affect; Age; Age Months; Archives; Artificial Intelligence; Behavior; Behavioral; Brain; Budgets; Characteristics; Child; Childhood; Classification; Clinical; Code; Communities; Competence; Critiques; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early Intervention; Emotional; Enrollment; Evaluation; Event; Family; Family history of; Foundations; Funding; Future; Goals; Hand; Health Services Accessibility; Hour; Improve Access; Individual; Infant; Intervention; Learning; Life; Lifestyle-related condition; Literature; Logistics; Machine Learning; Measures; Methodology; Methods; Online Systems; Parents; Participant; Peer Review; Performance; Process; Property; Psychometrics; Public Health Applications Research; Reaction; Reporting; Research; Research Design; Resources; Review Literature; Risk; Sampling; Screening Result; Screening procedure; Sensitivity and Specificity; Subgroup; Symptoms; System; Time; Toddler; Training; United States; United States National Institutes of Health; Writing; autism spectrum disorder; autistic children; automated algorithm; computer program; cost; detection method; disability; disorder risk; experience; falls; family burden; improved; infancy; innovation; instrument; machine learning algorithm; machine learning method; mobile application; mobile computing; new technology; novel; parent grant; prevent; psychologic; response; screening; systematic review; validation studies

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）的迹象出现在生命的第一年，在许多儿童，但诊断是 在美国，一般要晚得多，平均年龄为4岁。早期干预是高度 对患有ASD的幼儿有效，但通常保留给正式诊断的儿童， 尽早准确识别势在必行。一种筛查工具，可以识别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。在本申请中，我们 提出了两种新的基于视频的方法来检测ASD的第一年的生活。首先，我们将验证一个 最近开发的仪器，视频参考自闭症婴儿评级系统（VIRSA），在一般情况下， 婴儿的社区样本。VIRSA是一个简短的基于网络的工具，利用视频演示，而不是 而不是书面行为描述来检测自闭症的迹象。它利用了数千小时的 通过以前的NIH资助获得的收集和手工编码的视频。视频展示了一系列 行为和发展能力呈现给父母，由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VIRSA具有良好的心理测量特性 当由先前有ASD经历的父母使用时（即，有一个年龄较大的受影响的孩子），并证明 它能够区分婴儿在生命的第一年发展ASD。在目标1中，我们将检查 这项措施适用于那些没有自闭症家族史的自闭症患者的父母。在目标2中，我们建议 利用视频进行ASD检测的另一种创新方法。机器学习是人工智能的一种应用。 计算机程序“学习”和调整自己以响应训练数据的智能， 它们是公开的，提高了性能，并推广到新的数据，而无需显式地 程序化的。我们建议使用来自VIRSA的视频，之前在我们的初始验证中演示过 研究对ASD的早期迹象敏感，作为开发机器学习算法的训练输入， ASD相关行为的自动检测。巨大的视频档案可用于这个项目，与手- 编码的时间戳行为标签，是机器学习的非常有价值的资源。目标2将奠定 为未来尝试开发用于ASD识别的基于视频的移动的应用程序奠定了基础，这需要 经验证的分类器，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， 技术，包括移动的平台，视频和机器学习方法，以检测婴儿期的ASD风险。 这些措施将具有重要的公共卫生应用，包括筛查大型社区 样本和纵向跟踪儿童环境中的发展，以确定需要评估的儿童。 在婴儿期识别ASD将在最佳年龄提供治疗，此时大脑最具可塑性， 这可以减轻残疾，并可能防止出现以后出现的症状。

项目成果

PREDICTING AUTISM DIAGNOSIS USING IMAGE WITH FIXATIONS AND SYNTHETIC SACCADE PATTERNS.

• DOI: 10.1109/icmew.2019.00125
• 发表时间: 2019-07
• 期刊: ... IEEE International Conference on Multimedia and Expo workshops. IEEE International Conference on Multimedia and Expo
• 作者: Wu C;Liaqat S;Cheung SC;Chuah CN;Ozonoff S
• 通讯作者: Ozonoff S

Machine Learning Based Autism Spectrum Disorder Detection from Videos.

• DOI: 10.1109/healthcom49281.2021.9398924
• 发表时间: 2021-03
• 期刊: Healthcom. International Conference on e-Health Networking, Applications and Services
• 作者: Wu C;Liaqat S;Helvaci H;Cheung SS;Chuah CN;Ozonoff S;Young G
• 通讯作者: Young G

Differentially Private Generative Adversarial Networks with Model Inversion.

• DOI: 10.1109/wifs53200.2021.9648378
• 发表时间: 2021-12
• 期刊: Proceedings of the ... IEEE International Workshop on Information Forensics and Security. IEEE International Workshop on Information Forensics and Security
• 作者: Chen, Dongjie;Cheung, Sen-ching Samson;Chuah, Chen-Nee;Ozonoff, Sally
• 通讯作者: Ozonoff, Sally