Evaluation of machine learning to mobilize detection and therapy of developmental delay in children

机器学习的评估以动员儿童发育迟缓的检测和治疗

• 批准号: 9297669
• 负责人: Dennis Paul Wall
• 金额: $ 19.63万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-10 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9297669
• 关键词: Address; Age; Android; Autistic Disorder; Behavior; Behavior Therapy; Behavioral; Biomedical Engineering; Caregivers; Caring; Child; Classification; Clinic; Clinical; Complement; Cues; Data; Data Set; Detection; Development; Developmental Delay Disorders; Developmental Therapeutics Program; Devices; Diagnosis; Diagnostic; Early Intervention; Educational process of instructing; Emotions; Employee Strikes; Environment; Evaluation; Exhibits; Eye; Face; Facial Expression; Facial Expression Recognition; Family; Feedback; Funding; Future; Glass; Goals; Gold; Health Technology; Home environment; Individual; Interview; Liquid substance; Longitudinal Studies; Machine Learning; Measurable; Measurement; Measures; Methods; Operating System; Parents; Participant; Patients; Pediatric Hospitals; Phenotype; Process; Provider; Records; Research; Research Proposals; Risk; Risk Assessment; Running; Schedule; Self-Direction; Sensitivity and Specificity; Severities; Specificity; Speed; System; Testing; Therapeutic; Therapeutic Intervention; Time; Validation; Work; behavior measurement; care delivery; clinical care; data archive; data mining; design; digital; experimental study; handheld equipment; human-in-the-loop; improved; innovation; instrument; intervention program; personalized care; personalized medicine; programs; prototype; social; social learning; social skills; stem; tool; trial design

Project Summary Access to diagnosis of autism and to invaluable early interventional therapy is severely hampered by the imbalance between the number of children needing care and the inadequate number of clinical practitioners who can deliver that care. This numerical imbalance is unlikely to change in the near future, and therefore there is an urgent need and an exciting opportunity to innovate new methods of care delivery that can appropriately empower caregivers of children at risk for or with a diagnosis of autism, and that capitalize on mobile tools and wearable devices. Using machine learning and large-scale data mining, we have built a mobile system to quantify and track the severity of autism that takes only minutes of a caregiver’s time and that has promise for repeat use at home due to its speed, accuracy and mobility. We have concomitantly developed a machine-learning system for automatic facial expression recognition that runs on Google Glass and delivers real time social cues to individuals with autism in that child’s natural environment. Our goal in this research program is to work with our clinical colleagues at Stanford’s Autism Center to test and refine these complementary machine-learning systems for accuracy and optimal use by families and their child with autism from their natural environments. We will then combine the two systems in a multi-month longitudinal trial designed to harness our mobilized machine learning tool to quantitatively measure the efficacy of our Autism Glasses as a therapeutic assistant that functions in real time and within the child’s natural environment. Our proposed experiments with at least 40 subjects at risk for developmental delay promise to demonstrate how to leverage digital health technologies to improve, mobilize and quicken the detection and treatment of autism. The work also will result in a new and unique dataset that validates the ability to bring the social learning process outside of the clinic and into the real world, leading to a faster, more fluid way for children with autism to gain social skills. We also expect our work to show how measurable indicators of behavioral improvement during therapy will facilitate the process of tracking progress on an increasingly more granular scale, and hopefully set the stage for more effective, precise and personalized treatment.

项目摘要 自闭症的诊断和宝贵的早期介入治疗严重受阻于 需要护理的儿童人数和临床医生人数不足之间的不平衡 谁能提供这样的照顾。这种数字失衡在不久的将来不太可能改变，因此 迫切需要和令人兴奋的机会是创新提供护理的新方法，这种方法可以 适当地授权有自闭症风险或诊断为自闭症儿童的照顾者，并利用这一点 移动工具和可穿戴设备。利用机器学习和大规模数据挖掘，我们已经建立了一个 移动系统，量化和跟踪自闭症的严重程度，只需照顾者几分钟的时间，而且 由于其速度、准确性和机动性，有望在家中重复使用。我们同时发展了 一个用于自动面部表情识别的机器学习系统，运行在谷歌眼镜上，并提供 针对儿童自然环境中的自闭症患者的实时社交提示。我们这项研究的目标是 计划是与我们在斯坦福大学自闭症中心的临床同事合作，测试和完善这些 为家庭及其自闭症儿童提供准确和最佳使用的互补性机器学习系统 从他们的自然环境中。然后，我们将在几个月的纵向试验中将这两个系统结合起来 旨在利用我们的移动机器学习工具来定量衡量自闭症的疗效 眼镜作为一种治疗辅助工具，在儿童的自然环境中实时发挥作用。我们的 对至少40名有发育迟缓风险的受试者进行的拟议实验承诺展示如何 利用数字健康技术改进、动员和加快自闭症的检测和治疗。 这项工作还将产生一个新的和独特的数据集，验证带来社交学习的能力 走出诊所，进入真实世界，为自闭症儿童带来了一种更快、更流畅的方式 以获得社交技能。我们还希望我们的工作能显示出行为改善的可衡量指标 在治疗期间，将促进在越来越细粒度的范围内跟踪进展的过程，并且 希望为更有效、更精确和更个性化的治疗奠定基础。