Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings.

社交互动缺陷在许多精神疾病中都很明显，特别是在自闭症谱系障碍 (ASD) 中，但很难客观评估。我们提出了一种自动量化社交互动缺陷生物标志物的数字工具：模拟互动任务（SIT），它需要通过视频进行标准化的 7 分钟模拟对话，并对面部表情、凝视行为和声音特征进行自动分析。在一项针对 37 名无智力障碍的 ASD 成年人和 43 名健康对照者的研究中，我们展示了该工具作为诊断工具以及更好地描述 ASD 相关社会表型的潜力。使用机器学习工具，仅根据面部表情和声音特征，我们就能以 73% 的准确度、67% 的敏感度和 79% 的特异性来检测 ASD 个体。自闭症谱系障碍患者的特征尤其是社交微笑和面部模仿的减少，以及较高的语音基频和和声比。基于计算机的分析的时间效率和成本效益超过了多数投票，并且与临床专家评级相当。