Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.

IF 2.6 4区医学 Q2 CLINICAL NEUROLOGY

Movement Disorders Clinical Practice Pub Date : 2024-07-07 DOI:10.1002/mdc3.14158

Ronja Schappert, Julius Verrel, Nele Sophie Brügge, Frédéric Li, Theresa Paulus, Leonie Becker, Tobias Bäumer, Christian Beste, Veit Roessner, Sebastian Fudickar, Alexander Münchau

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引用次数: 0

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.

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基于视频的图雷特综合征自动诊断方法。

背景：抽搐是诊断吉勒-德拉图雷特综合征（GTS）的主要依据。基于视频的抽动评估非常耗时：目的：评估基于视频的自动抽动检测在区分成人 GTS 患者和健康对照组（HC）参与者视频方面的潜力：使用交叉验证逻辑回归法对患有 GTS 的成人（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搐/多余动作的数量和时间结构进行分类：从抽搐数量（均衡准确率为 87.9%）和抽搐集群数量（90.2%）两方面对视频进行分类的准确率都很高。逻辑回归预测概率提供了诊断可信度的分级衡量标准。专家对约 25% 的低置信度预测进行审查，可确保总体分类准确率超过 95%：基于视频的自动方法在支持抽搐症的定量评估和临床决策方面具有巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Movement Disorders Clinical Practice CLINICAL NEUROLOGY-

CiteScore

4.00

自引率

7.50%

发文量

218

期刊介绍： Movement Disorders Clinical Practice- is an online-only journal committed to publishing high quality peer reviewed articles related to clinical aspects of movement disorders which broadly include phenomenology (interesting case/case series/rarities), investigative (for e.g- genetics, imaging), translational (phenotype-genotype or other) and treatment aspects (clinical guidelines, diagnostic and treatment algorithms)