Automated behavior analysis are promising tools to overcome current assessment limitations in psychiatry. At 9 months of age, we recorded 32 infants with West syndrome (WS) and 19 typically developing (TD) controls during a standardized mother-infant interaction. We computed infant hand movements (HM), speech turn taking of both partners (vocalization, pause, silences, overlap) and motherese. Then, we assessed whether multimodal social signals and interactional synchrony at 9 months could predict outcomes (autism spectrum disorder (ASD) and intellectual disability (ID)) of infants with WS at 4 years. At follow-up, 10 infants developed ASD/ID (WS+). The best machine learning reached 76.47% accuracy classifying WS vs. TD and 81.25% accuracy classifying WS+ vs. WS-. The 10 best features to distinguish WS+ and WS- included a combination of infant vocalizations and HM features combined with synchrony vocalization features. These data indicate that behavioral and interaction imaging was able to predict ASD/ID in high-risk children with WS.

中文翻译：

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9个月大时的行为和互动成像可预测患有West综合征的高危婴儿的自闭症/智力障碍。

自动化的行为分析是克服当前精神病学评估局限性的有前途的工具。在9个月大时，我们记录了32名在标准的母婴互动过程中患有West综合征（WS）的婴儿和19名典型发展中（TD）的对照。我们计算了两个伙伴的婴儿手部运动（HM），语音转向（发声，暂停，沉默，重叠）和母语。然后，我们评估了9个月时的多模态社交信号和互动同步是否可以预测4岁WS婴儿的结局（自闭症谱系障碍（ASD）和智力障碍（ID））。在随访中，有10名婴儿出现了ASD / ID（WS +）。最好的机器学习达到了WS与TD的分类精度为76.47％，以及WS +与WS-的分类精度为81.25％。区分WS +和WS-的10个最佳功能包括结合婴儿发声和HM功能以及同步发声功能。这些数据表明行为和交互作用成像能够预测WS高危儿童的ASD / ID。