Dubey Indu, Bishain Rahul, Dasgupta Jayashree, Bhavnani Supriya, Belmonte Matthew K, Gliga Teodora, Mukherjee Debarati, Lockwood Estrin Georgia, Johnson Mark H, Chandran Sharat, Patel Vikram, Gulati Sheffali, Divan Gauri, Chakrabarti Bhismadev
University of Reading, UK.
University of Nottingham, UK.
Autism. 2024 Mar;28(3):755-769. doi: 10.1177/13623613231182801. Epub 2023 Jul 17.
Autism is diagnosed by highly trained professionals- but most autistic people live in parts of the world that harbour few or no such autism specialists and little autism awareness. So many autistic people go undiagnosed, misdiagnosed, and misunderstood. We designed an app (START) to identify autism and related conditions in such places, in an attempt to address this global gap in access to specialists. START uses computerised games and activities for children and a questionnaire for parents to measure social, sensory, and motor skills. To check whether START can flag undiagnosed children likely to have neurodevelopmental conditions, we tested START with children whose diagnoses already were known: Non-specialist health workers with just a high-school education took START to family homes in poor neighbourhoods of Delhi, India to work with 131 two-to-seven-year-olds. Differences between typically and atypically developing children were highlighted in all three types of skills that START assesses: children with neurodevelopmental conditions preferred looking at geometric patterns rather than social scenes, were fascinated by predictable, repetitive sensory stimuli, and had more trouble with precise hand movements. Parents' responses to surveys further distinguished autistic from non-autistic children. An artificial-intelligence technique combining all these measures demonstrated that START can fairly accurately flag atypically developing children. Health workers and families endorsed START as attractive to most children, understandable to health workers, and adaptable within sometimes chaotic home and family environments. This study provides a proof of principle for START in digital screening of autism and related conditions in community settings.
自闭症由训练有素的专业人员进行诊断——但大多数自闭症患者生活在世界上几乎没有或根本没有自闭症专家且对自闭症认知度很低的地区。因此，许多自闭症患者未被诊断、被误诊或被误解。我们设计了一款应用程序（START），用于在这些地区识别自闭症及相关病症，以解决全球在获取专家诊断方面的差距。START使用针对儿童的电脑游戏和活动以及针对家长的问卷来衡量社交、感官和运动技能。为了检验START能否标记出可能患有神经发育病症但未被诊断的儿童，我们对诊断结果已知的儿童进行了START测试：仅受过高中教育的非专业卫生工作者带着START前往印度德里贫困社区的家庭，与131名2至7岁的儿童合作。在START评估的所有三种技能类型中，发育正常和发育异常儿童之间的差异都很明显：患有神经发育病症的儿童更喜欢看几何图案而非社交场景，对可预测、重复性的感官刺激着迷，并且在精确手部动作方面有更多困难。家长对调查的回答进一步区分了自闭症儿童和非自闭症儿童。一种结合所有这些测量方法的人工智能技术表明，START能够相当准确地标记出发育异常的儿童。卫生工作者和家庭认可START对大多数儿童有吸引力，卫生工作者能够理解，并且在有时混乱的家庭环境中也适用。这项研究为START在社区环境中对自闭症及相关病症进行数字筛查提供了原理证明。
