Clinical research in autism has recently witnessed promising digital phenotyping results, mainly focused on single feature extraction, such as gaze, head turn on name-calling or visual tracking of the moving object. The main drawback of these studies is the focus on relatively isolated behaviors elicited by largely controlled prompts. We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts. For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing peers. Our model achieves an accuracy of 80.9% (F1 score: 0.818; precision: 0.784; recall: 0.854) with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain. Provided the non-invasive and affordable nature of computer vision, our approach carries reasonable promises that a reliable machine-learning-based ASD screening may become a reality not too far in the future.

中文翻译：

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使用基于2D视频的姿势估计来自动预测学龄前儿童的自闭症谱系障碍

自闭症的临床研究最近目睹了令人鼓舞的数字表型化结果，主要集中在单特征提取上，例如凝视，抬头打头或移动物体的视觉跟踪。这些研究的主要弊端在于将注意力集中在很大程度上受控制的提示引起的相对孤立的行为上。我们认识到，尽管诊断过程可以理解特定行为的索引，但ASD还具有广泛的障碍，通常会超越单一的行为行为。例如，非典型的非语言行为表现为非典型的姿势和动作的整体模式，较少使用的手势，并且通常与视觉接触，面部表情，言语脱钩。这里，我们测试了以下假设：在社交互动的非言语方面训练的深度神经网络可以有效地区分患有ASD的儿童和他们通常发育的同伴。我们的模型达到80.9％的准确性（F1得分：0.818；精度：0.784；召回率：0.854），其预测概率与自闭症症状在社会影响以及重复和受限行为领域的总体水平呈正相关。提供计算机视觉的非侵入性和负担得起的性质，我们的方法具有合理的前景，即基于可靠的基于机器学习的ASD筛选可能在不久的将来成为现实。854）的预测概率与自闭症症状在社交影响以及重复和受限行为领域的总体水平成正相关。提供计算机视觉的非侵入性和负担得起的性质，我们的方法具有合理的前景，即基于可靠的基于机器学习的ASD筛选可能在不久的将来成为现实。854）的预测概率与自闭症症状在社会影响以及重复性和受限行为领域的总体水平呈正相关。提供计算机视觉的非侵入性和负担得起的性质，我们的方法具有合理的前景，即基于可靠的基于机器学习的ASD筛选可能在不久的将来成为现实。