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

项目摘要 神经精神障碍是非传染性疾病导致残疾的最大原因 占全球疾病负担的14%。目前的护理标准受到 主观性、交付不一致以及候补名单不断增加而访问受限。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的健康设备， 限制在临床环境中使用，有很大的潜力来补充甚至取代标准的某些方面。 照顾。我们建议为最紧迫的心理健康负担之一开发一种新的信息学解决方案， 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，和许多其他神经精神疾病的高度代表性。我们发明了一种原型 一个名为Guess What（guesswhat.stanford.edu）（GW）的移动的系统，它将摄像机的焦点转向 孩子通过与他/她的社会伙伴的流畅的社会参与，加强亲社会学习， 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以社交和情感为中心的提示， 就在与孩子玩耍的人的眼睛上方。但更多的是，作为一个家庭为基础的重复使用 系统中，GW使用计算机视觉算法和集成到游戏中的情感分类器来检测情感 通过手机的前置摄像头在孩子的脸上，自动找到与显示的提示，而 捕捉诸如凝视、目光接触和共同注意的特征。与20多名自闭症患者的初步工作 孩子们得到了积极的用户反馈，这是父母和孩子高度参与的证据， 重要的是，社会化方面有临床意义的进展的证据。一次会议产生90秒的 丰富了社交视频和传感器数据，为游戏本身提供了一个令人兴奋的机会， 生成标记的计算机视觉库，使开发具有更高诊断能力的更好模型成为可能 精准向前我们提出的项目将表明，GW可以（a.）作为一种移动的治疗， 家庭反复使用，以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， （B.）作为一个分布式系统，众包获取人工智能模型的新标记图像库 它可以自动分类与自闭症相关的诊断特征，并扩展到其他心理健康领域， (and甚至超越）。

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).