A Mobile Game for Domain Adaptation and Deep Learning in Autism Healthcare

用于自闭症医疗领域适应和深度学习的手机游戏

• 批准号: 10596139
• 负责人: Dennis Paul Wall
• 金额: $ 63.7万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-02 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10596139
• 关键词: 8 year old; Accounting; Address; Age; Agreement; Algorithms; Android; Anger; Animals; Artificial Intelligence; Awareness; Behavior; Behavior Therapy; Caring; Cellular Phone; Child; Childhood; Classification; Clinic; Clinical; Complement; Computer Vision Systems; Computer software; Data; Data Science; Detection; Development; Devices; Diagnostic; Disease; Distributed Systems; Dose; Emotional; Emotions; Enrollment; Eye; Face; Family; Feedback; Forehead; Foundations; Frequencies; Frustration; Future; Gestures; Happiness; Healthcare; Healthcare Systems; Home; Hour; Human; Image; Incidence; Individual; Informatics; Label; Language; Learning; Left; Libraries; Life; Liquid substance; Manuals; Measures; Mental Health; Methods; Modeling; Motivation; Outcome; Parents; Performance; Periodicals; Phenotype; Play; Process; Social Behavior; Social Interaction; Socialization; Speed; Symptoms; System; Telephone; Testing; Training; Waiting Lists; Work; applied behavior analysis; autism spectrum disorder; autistic children; burden of illness; computer generated; convolutional neural network; cost; crowdsourcing; data library; deep learning; deep learning model; deep neural network; design; digital tool; disability; feasibility testing; feature extraction; gaze; global health; image archival system; improved; innovation; interest; invention; joint attention; learning progression; mHealth; marine; neural network; neuropsychiatric disorder; neuropsychiatry; novel; personalized care; personalized diagnostics; personalized health care; prototype; repository; sensor; social; social communication; social contact; social engagement; social reciprocity; success; tool; treatment effect; treatment response

Project Summary Neuropsychiatric disorders are the single greatest cause of disability due to non-communicable disease worldwide, accounting for 14% of the global burden of disease. The current standards of care suffer from subjectivity, inconsistent delivery, and limited access with growing waitlists. New informatics solutions, in particular artificial intelligence (AI) that can port to more ubiquitous mobile health devices and that are not restricted for use in clinical settings, have great potential to complement or even replace aspects of the standards of care. We propose to develop a novel informatics solution for one of the most pressing mental health burdens, autism, which is up in incidence by more than 600% since 1990, among the fastest growing pediatric concerns today, and highly representative of many other neuropsychiatric conditions. We have invented a prototype mobile system called Guess What (guesswhat.stanford.edu) (GW) that turns the focus of the camera on the child through a fluid social engagement with his/her social partner that reinforces prosocial learning while simultaneously measuring the child’s developmental learning progress. At its simplest level, the GW app challenges the child to imitate social and emotion-centric prompts shown on the screen of a smartphone held just above the eyes of the individual with whom the child is playing. But more, as a home-based repeat-use system, GW uses computer vision algorithms and emotion classifiers integrated into gameplay to detect emotion in the child’s face via the phone’s front camera, automatically finding agreement with the displayed prompt, while capturing features such as gaze, eye contact, and joint attention. Preliminary work with more than 20 autistic children resulted in positive user feedback, evidence of high engagement for both the parents and children, and importantly, evidence of clinically meaningful gains in socialization. A single session produces 90 seconds of enriched social video and sensor data, opening up an exciting opportunity for the game play itself to passively generate labeled computer vision libraries that enable the development of better models with higher diagnostic precision going forward. Our proposed project will show that GW can (a.) serve as a mobile therapy that can be used repeatedly by families to target core deficits of autism while inherently tracking progress during use, and, (b.) serve as a distributed system to crowdsource the acquisition of new labeled image libraries for AI models that can automatically classify diagnostic features relevant to autism and extend to other sectors of mental health (and even beyond).

项目概要 神经精神疾病是非传染性疾病导致残疾的最大单一原因 占全球疾病负担的 14%。目前的护理标准存在以下问题 主观性、交付不一致以及等候名单不断增加而导致的访问受限。新的信息学解决方案， 特定的人工智能（AI）可以移植到更普遍的移动健康设备上，但不是 仅限于临床环境中使用，具有补充甚至替代标准的巨大潜力 的照顾。我们建议针对最紧迫的心理健康负担之一开发一种新颖的信息学解决方案， 自 1990 年以来，自闭症的发病率上升了 600% 以上，是增长最快的儿科问题之一 今天，并且高度代表许多其他神经精神疾病。我们发明了一个原型 名为 Guess What (guesswhat.stanford.edu) (GW) 的移动系统，可将相机的焦点转向 孩子通过与他/她的社交伙伴进行流畅的社交活动来加强亲社会学习，同时 同时测量孩子的发展学习进度。在最简单的层面上，GW 应用程序 挑战孩子模仿智能手机屏幕上显示的社交和以情感为中心的提示 就在与孩子玩耍的人的眼睛上方。但更多的是，作为家庭重复使用 系统中，GW 使用计算机视觉算法和集成到游戏中的情绪分类器来检测情绪 通过手机的前置摄像头在孩子的脸上自动寻找与显示的提示一致的内容，同时 捕捉凝视、目光接触和共同注意力等特征。与20多名自闭症患者的初步工作 孩子们获得了积极的用户反馈，证明父母和孩子都高度参与，并且 重要的是，有临床意义的社交收益的证据。单个会话产生 90 秒的 丰富的社交视频和传感器数据，为游戏本身被动地提供了一个令人兴奋的机会 生成标记的计算机视觉库，以便开发具有更高诊断能力的更好模型 精度向前推进。我们提出的项目将表明 GW 可以 (a.) 作为一种移动疗法，可以 被家庭反复使用来针对自闭症的核心缺陷，同时在使用过程中固有地跟踪进展，并且， (b.) 作为分布式系统，众包获取用于 AI 模型的新标记图像库 可以自动对与自闭症相关的诊断特征进行分类，并扩展到心理健康的其他领域 （甚至超越）。