PDF(Pubmed)
Abstract：
UNASSIGNED: The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children\'s homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the \'proof-of-concept\' stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children\'s behaviours or speech. Computerised analysis of children\'s interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.
摘要：
未经评估：寻找自闭症儿童的挑战，尽早找到他们，为他们和他们的家人带来改变，在人力和物力资源短缺的地区变得更加重要。资源匮乏会拖延干预措施，转化为结果的贫困。数字工具具有减少这种延迟的潜力，因为它们可以由儿童家中的非专业人员管理，学校或其他日常环境，他们可以客观地测量各种自闭症行为，并且可以自动进行分析，而无需计算机或统计学专家。这篇文献综述旨在识别和描述用于筛查可能有自闭症风险的儿童的数字工具。这些工具主要处于“概念验证”阶段。两种便携式(笔记本电脑，移动电话,智能玩具)和固定(台式电脑，虚拟现实平台)技术用于呈现电脑游戏，或记录孩子的行为或言语。对儿童与这些技术的互动的计算机分析区分了患有和没有自闭症的儿童，有希望的结果。评估社会反应以及手部和身体运动的任务是最可靠的区分自闭症和典型发育中的儿童。这种数字工具在大规模早期识别自闭症谱系障碍风险方面具有巨大潜力。接下来的步骤应该是进一步验证这些工具，并评估它们在各种环境中的适用性。至关重要的是，来自全球服务不足社区的利益相关者必须参与这项研究，以免它无法抓住这些利益相关者面临的问题。
