Abstract：
BACKGROUND: The occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video-based tic assessments are time consuming.
OBJECTIVE: The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants.
METHODS: The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression.
RESULTS: Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%.
CONCLUSIONS: Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.
OBJECTIVE: The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants.
METHODS: The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression.
RESULTS: Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%.
CONCLUSIONS: Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.
摘要：
背景：抽动的发生是诊断GillesdelaTourette综合征（GTS）的主要依据。基于视频的tic评估是耗时的。
目的：目的是评估基于视频的自动抽动检测在区分患有GTS的成年人和健康对照（HC）参与者的视频方面的潜力。
方法：使用来自GTS成人（来自42名参与者的107个视频）和匹配的HC的视频中自动检测到的抽动/额外运动的数量和时间结构，使用交叉验证的逻辑回归对视频进行分类。
结果：从抽动症的数量（平衡精度为87.9％）和抽动症簇的数量（90.2％）对视频进行了高精度分类。逻辑回归预测概率提供了诊断置信度的分级度量。对大约25％的低置信度预测进行专家审查可以确保总体分类准确性高于95％。
结论：基于视频的自动化方法有很大的潜力支持抽动障碍的定量评估和临床决策。
目的：目的是评估基于视频的自动抽动检测在区分患有GTS的成年人和健康对照（HC）参与者的视频方面的潜力。
方法：使用来自GTS成人（来自42名参与者的107个视频）和匹配的HC的视频中自动检测到的抽动/额外运动的数量和时间结构，使用交叉验证的逻辑回归对视频进行分类。
结果：从抽动症的数量（平衡精度为87.9％）和抽动症簇的数量（90.2％）对视频进行了高精度分类。逻辑回归预测概率提供了诊断置信度的分级度量。对大约25％的低置信度预测进行专家审查可以确保总体分类准确性高于95％。
结论：基于视频的自动化方法有很大的潜力支持抽动障碍的定量评估和临床决策。
