Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.
Max Planck Institute of Psychiatry, Munich, Germany.
Sci Rep. 2024 Mar 7;14(1):5663. doi: 10.1038/s41598-024-56098-y.
Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.
预测建模策略越来越多地被研究为克服自闭症谱系障碍诊断分类中的临床瓶颈的一种手段。然而，尽管一些研究结果在诊断标志物研究方面很有前景，但这些方法中的许多方法缺乏足够和有效的扩展，无法转化为日常临床实践。在这项研究中，我们的目的是探索在向成年过渡的儿童和青少年临床样本中使用真实世界的自闭症诊断访谈的客观计算机视觉视频分析来预测诊断。具体来说，我们在患者-临床医生对的自闭症诊断观察量表（ADOS-2）访谈中记录的人际同步数据上训练了支持向量机学习模型。我们的模型能够以 63.4%的平衡准确率对涉及自闭症患者的对（n=56）进行分类，而对包括其他精神科诊断患者的对（n=38）进行分类。进一步的分析表明，我们的分类指标与临床评分之间没有显著关联。我们认为，鉴于我们的分类器在年龄和诊断方面高度异质的样本中表现出高于平均水平的性能，并且只需进行少量调整，这种高度可扩展的方法为自闭症的未来诊断标志物研究提供了可行的途径。
