Quantification of Tics in Tourette Syndrome

项目摘要/摘要 多发性抽动症(TS)是一种慢性儿童起病的神经发育障碍，影响1-3%的人 并与不良的功能影响有关。TS的特点是抽搐，它是非自愿的，重复的 动作和发声。目前TS的临床护理面临的一个挑战是缺乏客观的、定量的、 可扩展的工具，用于测量抽搐，以进行诊断和症状严重程度监控。整体而言 拟议研究的目标是在大量、多样化的社区样本中使用基于视频的方法来提供信息 抽搐的定量和自动表型鉴定。本研究建立在以前工作的基础上，包括：1)基于视频的 有训练有素的人类评分员的观察方法，以量化用于研究目的的抽搐，2)计算机视觉和 用于运动分析和医疗诊断辅助的机器学习技术，以及3)初步数据 指出有监督的学习方法可用于高精度地自动检测眼部抽搐。在……里面 目标1，将使用远程和远程方法收集N=1,000名抽搐患者的视频和临床数据 基于互联网的方法。将使用深度表型方法来定量描述表型 可观察到的运动和声带痉挛的频谱，经验性地得出抽搐严重程度基准，并识别患者 子组。在目标2中，我们的计算机视觉团队将使用有监督的机器学习方法来处理目标1的数据 创造出一种能够检测最常见抽搐的算法。目标3将前瞻性地测试目标2 算法在N=60名在单独的临床试验中完成TS治疗的患者中建立该算法的 对变化的敏感度和现行金标准抽动严重程度的收敛效度。这个项目 这将使我们第一次能够在大的社区样本中量化可观察到的抽搐的频谱， 对诊断决策和患者教育有直接临床意义的知识。 目标2将产生一种计算机算法，能够自动量化最常见的运动痉挛， 为抽动障碍筛查开发准确、临床有效和可扩展的评估的关键下一步， 临床试验中的诊断、治疗决策和症状量化。

PROJECT SUMMARY/ABSTRACT Tourette Syndrome (TS) is a chronic, childhood-onset neurodevelopmental disorder that affects 1-3% of people and is associated with adverse functional impacts. TS is characterized by tics, which are involuntary, repetitive movements and vocalizations. A current challenge in clinical care for TS is the lack of objective, quantitative, scalable tools to measure tics for the purposes of diagnosis and symptom severity monitoring. The overall objective of the proposed study is to use video-based methods in a large, diverse community sample to inform quantitative and automated phenotyping of tics. This study builds on prior work, including: 1) video-based observational methods with trained human raters to quantify tics for research purposes, 2) computer vision and machine learning techniques for movement analysis and medical diagnostic aids, and 3) preliminary data indicating supervised learning methods can be used to automate detection of eye tics with high accuracy. In Aim 1, videos and clinical data from N = 1,000 individuals with tics will be collected using remote and internet-based methods. A deep phenotyping approach will be used to quantitatively describe the phenotypic spectrum of observable motor and vocal tics, empirically derive tic severity benchmarks, and identify patient subgroups. In Aim 2, our computer vision team will apply supervised machine learning methods to Aim 1 data to create an algorithm capable of detecting the most common tics. Aim 3 will prospectively test the Aim 2 algorithm in N = 60 patients who completed TS treatment in a separate clinical trial to establish the algorithm’s sensitivity to change and convergent validity with current gold-standard tic severity measurement. This project will enable us, for the first time, to quantify the spectrum of observable tics in a large community sample, knowledge that will have immediate clinical relevance for diagnostic decision making and patient education. Aim 2 will yield a computer algorithm capable of autonomously quantifying the most common motor tics, a critical next step toward developing accurate, clinically valid, and scalable assessments for tic screening, diagnosis, treatment decision making, and symptom quantification in clinical trials.