Quantification of Tics in Tourette Syndrome

抽动秽语综合征抽动的量化

• 批准号: 10635872
• 负责人: Christine A Conelea
• 金额: $ 61.11万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10635872
• 关键词: Adult; Affect; Aftercare; Agreement; Algorithms; Attention deficit hyperactivity disorder; Benchmarking; Blinking; Child; Childhood; Chronic; Classification; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinical assessments; Code; Communities; Computational algorithm; Computer Vision Systems; Data; Data Set; Decision Making; Detection; Development; Diagnosis; Diagnostic; Disparity in diagnosis; Dyskinetic syndrome; Education; Event; Eye; Face; Frequencies; Gilles de la Tourette syndrome; Goals; Head; Health Services Accessibility; Healthcare; Human; Individual; Internet; Interview; Knowledge; Label; Left; Machine Learning; Manuals; Measurement; Measures; Medical; Mental Depression; Methods; Monitor; Motor Tics; Movement; Neurodevelopmental Disorder; Neurology; Paper; Parkinson Disease; Patient Education; Patients; Persons; Pharmaceutical Preparations; Phenotype; Prospective cohort; Provider; Psychiatry; Psychology; Questionnaires; Research; Sampling; Severities; Shoulder; Symptoms; System; Techniques; Technology; Testing; Time; Training; Underrepresented Populations; Vision research; Vocal Tics; Work; clinical care; clinical diagnostics; clinically relevant; computer science; data acquisition; ethnoracial disparity; experience; feature extraction; insight; learning algorithm; learning strategy; machine learning method; minority children; movement analysis; novel; patient subsets; phenomenological models; prospective test; psychostimulant; racial minority; screening; supervised learning; tic suppression; tool; vocalization

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.

项目概要/摘要 抽动秽语综合征 (TS) 是一种慢性、儿童期发病的神经发育障碍，影响 1-3% 的人 TS 的特点是不自主的、重复性的抽动。 当前 TS 临床护理面临的挑战是缺乏客观、定量、 用于测量抽动的可扩展工具，用于诊断和症状严重程度监测。 拟议研究的目标是在大型、多样化的社区样本中使用基于视频的方法来告知 本研究建立在之前的工作基础上，包括：1) 基于视频的研究。 使用训练有素的人类评估员的观察方法来量化研究目的的抽动，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 表明监督学习方法可用于高精度地自动检测眼睛抽动。 目标 1，将使用远程和远程收集 N = 1,000 名抽动患者的视频和临床数据 基于互联网的方法将用于定量描述表型。 可观察到的运动和发声抽动的频谱，凭经验得出抽动严重程度基准，并识别患者 在目标 2 中，我们的计算机视觉团队将对目标 1 数据应用监督机器学习方法。 为了创建能够检测最常见抽动的算法，Aim 3 将前瞻性地测试 Aim 2。 在一项单独的临床试验中，对 N = 60 名完成 TS 治疗的患者进行了算法分析，以建立该算法的 该项目对变化的敏感性和当前金标准抽动严重程度测量的收敛有效性。 将使我们第一次能够量化大型社区样本中可观察到的抽动的范围， 与诊断决策和患者教育具有直接临床相关性的知识。 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抽搐， 为抽动症筛查开发准确、临床有效且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。