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Quantification of Tics in Tourette Syndrome

抽动秽语综合征抽动的量化

基本信息

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
项目类别：
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

项目摘要

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.

项目总结/文摘