Sapiro Guillermo, Hashemi Jordan, Dawson Geraldine
Electrical and Computer Engineering, Computer Sciences, Biomedical Engineering, and Math, Duke University, Durham, NC, 27707, United States.
Electrical and Computer Engineering, Duke University, Durham, NC, 27707, United States.
Curr Opin Biomed Eng. 2019 Mar;9:14-20. doi: 10.1016/j.cobme.2018.12.002. Epub 2018 Dec 18.
Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in neurodevelopmental disorders, including autism spectrum disorder. Such behavioral ratings are subjective, require significant clinician expertise and training, typically do not capture data from the children in their natural environments such as homes or schools, and are not scalable for large population screening, low-income communities, or longitudinal monitoring, all of which are critical for outcome evaluation in multisite studies and for understanding and evaluating symptoms in the general population. The development of computational approaches to standardized objective behavioral assessment is, thus, a significant unmet need in autism spectrum disorder in particular and developmental and neurodegenerative disorders in general. Here, we discuss how computer vision, and machine learning, can develop scalable low-cost mobile health methods for automatically and consistently assessing existing biomarkers, from eye tracking to movement patterns and affect, while also providing tools and big data for novel discovery.
尽管最近分子遗传学和神经科学取得了重大进展，但基于临床观察的行为评分仍然是筛查、诊断和评估神经发育障碍（包括自闭症谱系障碍）结果的金标准。此类行为评分具有主观性，需要临床医生具备丰富的专业知识和培训，通常无法获取儿童在家庭或学校等自然环境中的数据，并且无法扩展用于大规模人群筛查、低收入社区或纵向监测，而所有这些对于多中心研究中的结果评估以及理解和评估普通人群中的症状都至关重要。因此，开发用于标准化客观行为评估的计算方法，特别是在自闭症谱系障碍以及一般发育和神经退行性疾病中，是一项尚未得到满足的重大需求。在此，我们讨论计算机视觉和机器学习如何能够开发可扩展的低成本移动健康方法，用于自动且一致地评估现有的生物标志物，从眼动追踪到运动模式和情感，同时还能为新发现提供工具和大数据。
