An Automated Early Motor Development Risk Screener from Observational Video Recordings of Infants and Toddlers

根据婴儿和幼儿的观察视频记录自动进行早期运动发育风险筛查

基本信息

批准号：
10065509
负责人：
BHARATH MODAYUR
金额：
$ 60.54万
依托单位：
BSOLUTIONS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-01 至 2022-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10065509
关键词：
Address Age Alberta province American Assessment tool Back Behavior Disorders Behavior Therapy Behavioral Birth Brain Catalogs Cerebral Palsy Child Childhood Clinical Code Cognitive Communication Communities Complex Data Detection Development Developmental Coordination Disorders Developmental Delay Disorders Diagnosis Diagnostic tests Early Intervention Early identification Evaluation Generations Genetic Goals Gold Human Infant Language Lead Link Machine Learning Manuals Measures Memory Methods Modeling Motor Movement Occupational Therapy Parents Pediatrics Phase Physical therapy Pilot Projects Play Process Provider Questionnaires Reporting Research Resources Risk Risk Assessment Role Schools Screening procedure Services Site Standardization Stream Supination System Testing Time Toddler Training Treatment Efficacy Video Recording Well Child Visits autism spectrum disorder base cognitive performance cohort cost cost effective critical period data acquisition deep learning developmental disease disability early screening experience flexibility follow-up high risk improved infant outcome innovation instrument learning network motor function improvement portability prevent prototype relating to nervous system screening screening services service intervention social social engagement spatial memory tool

项目摘要

Motor development plays a pivotal role in an infant’s overall development and has a cascading effect on social, cognitive, memory, and language ability. These functions can be compromised when motor development is disrupted; motor disruption is associated with generalized developmental delays. Early screening and identification of delays can allow early interventions with the goal of not just improving motor function but overall development in other domains. Experience with independent or motorized crawling is shown to improve spatial memory in infants. Active motor training, even as early as 3 months, is shown to achieve gains in object exploration and social engagement that persist long after the training sessions, indicating the benefits of early intervention. Children who get developmental screening are more likely to be identified with delays and receive early intervention services compared to those that only receive age-appropriate milestone checks as part of pediatric well-child visits. However, comprehensive motor screening, using standardized instruments, for developmental surveillance is labor intensive and requires specialized training in administration and evaluation. Shortage of resources to perform frequent, gold-standard, comprehensive motor assessments can lead to missed opportunities for early intervention and contribute negatively to lifelong outcomes for infants at risk for behavioral and developmental disorders. In the US, 15-20% of children have a developmental or behavioral disability, but less than a third of them get diagnosed before entering school, preventing opportunities for early intervention during a critical period of brain development. In Phase I, we built a prototype Human Action Recognition Engine (HARE) to automate extraction of infant motor movement from video; then utilized this engine to demonstrate the feasibility of an automated developmental risk screener by leveraging recent advances in machine learning to achieve state of the art accuracies in assessment of infant motor development. In Phase II, we aim to build out the risk screener, SCOREIT, as a clinically deployable, cost-effective, developmental risk assessment tool to identify children who need further clinical follow-up. SCOREIT has the potential to transform early developmental screening by bringing the power of comprehensive motor screening, usually administered only to at-risk children utilizing specialized resources, to underserved and economically-fragile communities. Less than a third of US children receive developmental screening. SCOREIT can bring risk screenings to the over 70% of children that currently do not receive them, increasing opportunities for early intervention for behavioral and developmental disorders. Additionally, the HARE system’s ability to automatically extract and quantify motor movements from video can be an enabler for explorations of early markers for behavioral and developmental disorders, significantly easing manual video coding burdens and accelerating the pace of discoveries.

运动发育在婴儿的整体发育中起着关键作用，并对社交、当运动发育障碍时，认知、记忆和语言能力可能会受到损害。紊乱；运动障碍与普遍的发育迟缓有关。识别延迟可以进行早期干预，其目标不仅是改善运动功能，而且是整体改善其他领域的发展表明，独立或机动爬行的经验可以改善空间。婴儿的主动运动训练，即使是在 3 个月大的时候，也被证明可以提高物体的记忆力。探索和社会参与在培训课程结束后持续很长时间，表明早期的好处干涉。接受发育筛查的儿童更有可能发现发育迟缓并尽早接受治疗与仅接受适合年龄的里程碑检查作为儿科治疗一部分的干预服务相比然而，使用标准化仪器进行全面的运动筛查，以促进发育。监测是劳动密集型工作，需要管理和评估方面的专门培训。执行频繁、黄金标准、全面的运动评估的资源可能会导致错过早期干预的机会，并对有行为风险的婴儿的终生结果产生负面影响在美国，15-20% 的儿童存在发育或行为障碍。其中不到三分之一的人在入学前得到诊断，从而阻碍了早期干预的机会在大脑发育的关键时期。在第一阶段，我们构建了一个原型人类动作识别引擎（HARE）来自动提取婴儿运动视频中的运动；然后利用该引擎来演示自动化开发的可行性风险筛选器利用机器学习的最新进展来实现最先进的准确性在第二阶段，我们的目标是建立风险筛查 SCOREIT 作为一个评估婴儿运动发育的工具。临床上可部署的、具有成本效益的发育风险评估工具，用于识别需要进一步治疗的儿童临床随访 SCOREIT 有潜力通过带来力量来改变早期发育筛查。全面的运动筛查，通常仅利用专门资源对高危儿童进行，服务不足和经济脆弱的社区中只有不到三分之一的儿童接受了发展教育。 SCOREIT 可以为目前未接受筛查的 70% 以上的儿童提供风险筛查，增加对行为和发育障碍进行早期干预的机会。 HARE 系统能够从视频中自动提取和量化运动运动，这可以推动探索行为和发育障碍的早期标记，显着简化手动视频编码负担并加快发现的步伐。