Computer vision and behavioral phenotyping: an autism case study.

Computer vision and behavioral phenotyping: an autism case study.
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DOI:
10.1016/j.cobme.2018.12.002
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发表时间:
2019-03
影响因子:
3.9
通讯作者:
--
中科院分区:
工程技术3区
文献类型:
--
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尽管分子遗传学和神经科学最近取得了重大进展,但基于临床观察的行为评分仍然是筛选、诊断和评估包括自闭症谱系障碍在内的神经发育障碍结果的金标准。这样的行为评级是主观的,需要大量的临床医生专业知识和培训,通常不会收集儿童在家庭或学校等自然环境中的数据,并且不能扩展到大规模人口筛查、低收入社区或纵向监测,所有这些都对多点研究的结果评估以及对普通人群中的症状的了解和评估至关重要。因此,标准化客观行为评估的计算方法的发展在自闭症谱系障碍以及一般的发育和神经退行性疾病中是一个重要的未得到满足的需求。在这里,我们讨论计算机视觉和机器学习如何开发可扩展的低成本移动健康方法,以自动和一致地评估现有的生物标记物,从眼睛跟踪到运动模式和影响,同时还为新发现提供工具和大数据。
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.
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