SCH: Computer Vision Algorithms to Detect Tics In Patients with Tourette Syndrome

SCH：用于检测抽动秽语综合征患者抽动的计算机视觉算法

基本信息

批准号：
10817272
负责人：
Joseph McGuire
金额：
$ 30万
依托单位：
UNIVERSITY OF PENNSYLVANIA
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：
2023-09-18 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10817272
关键词：
Adverse effects Affect Algorithms Architecture Attention Awareness Behavior Therapy Behavioral Biomedical Engineering Body part Categories Childhood Classification Clinical Clinical assessments Complex Computer Vision Systems Data Detection Discrimination Evaluation Exhibits FDA approved Face Feedback Gestures Gilles de la Tourette syndrome Goals Grain Health Health Professional Human Impairment Joints Knowledge Label Learning Learning Skill Machine Learning Modeling Movement Multiple Partners Ontology Outcome Participant Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Population Process Published Comment Recommendation Research System Techniques Technology Therapeutic Tic disorder Training Work Youth automated algorithm convolutional neural network deep learning design digital improved innovation innovative technologies learning strategy multimodality neuropsychiatry novel sensor side effect skeletal skills spatiotemporal supervised learning therapy outcome tool treatment response user-friendly

项目摘要

Tourette Syndrome (TS) onsets in childhood, affects 1% of the population, and causes substantial impairment. Health professionals recommend behavior therapy as the first-line treatment for TS due to its efficacy and adverse effect profile. In behavior therapy, patients learn tic management skills and are assigned “homework” to solidify skill learning. Core skills involve building awareness to tic occurrence and implementing behavioral strategies to inhibit tic expression upon such awareness. Youth who exhibit a treatment response to behavior therapy continue to benefit for 10 years. However, more than 50% of patients do not achieve a treatment response and rely upon FDA-approved medications that have detrimental health effects. A key challenge with behavior therapy is the reliance on a human practice partner for “homework” due to accessibility and accuracy. Our team will create an activity-based recognition system and algorithms to identify and classify tics across activities and sensor viewpoints in patients with TS. This system will evolve into a therapeutic tool (i.e., a “digital practice partner”) that is scalable, accessible, and accurate in detecting tics. This will enable patients to effectively practice behavior therapy skills and achieve optimal long-term outcomes. First, we will develop a multi-view sensor system to observe tics in patients with TS and refine our hierarchical ontology of tics to annotate data collected from our sensor system. This will enable both a clinically interpretable and fine-grained classification of tic and non-tic movements. Second, we will design a novel spatio-temporal CNN called Tic-Net for fine-grained detection of facial and upper body tics in video data from multiple viewpoints, which will rely on facial action unit intensities and interpretable upper body part features that we design, temporal segmentation and detection networks, as well as contrastive and self-supervised learning losses to detect tics without requiring large amounts of annotations. Third, we will design a novel spatio-temporal Transformer architecture called Tic-DETR for fine-grained tic detection, which captures long-range interactions among face action units and/or skeletal joints across multiple views as well as relations between tic instances to produce interpretable detections of tics of varying durations from multiple viewpoints. Finally, we will compare detection outcomes between our algorithm and a human practice partner, evaluate the robustness of algorithms across viewpoints, and assess its clinical interpretability.

抽动秽语综合症 (TS) 在儿童期发病，影响 1% 的人口，并造成严重损害。由于行为疗法的疗效和不良反应，健康专家建议将行为疗法作为 TS 的一线治疗方法。管理技能并被分配“家庭作业”来巩固技能学习，核心技能包括建立对抽动发生的认识并实施行为策略以抑制对行为疗法的治疗反应的青少年持续受益。多于50% 的患者没有达到治疗反应，并且依赖 FDA 批准的药物，这会损害健康。行为疗法的一个关键挑战是由于可及性和准确性而依赖人类实践伙伴的“作业”。创建基于活动的识别系统和算法，以识别和分类 TS 患者的活动和传感器观点的抽动。该系统将发展成为可扩展、可访问且准确的治疗工具（即“数字实践伙伴”）。在检测抽动时。这将使患者能够有效地练习行为治疗技能并实现最佳的长期结果。首先，我们将开发一个多视图传感器系统来观察 TS 患者的抽动，并完善我们的抽动分层本体来注释从我们的传感器收集的数据。这将使抽动和非抽动运动能够进行临床可解释的细粒度分类。其次，我们将设计一种名为 Tic-Net 的新型时空 CNN，用于对视频中的面部和上半身抽动进行细粒度检测。来自多个视角的数据，这将依赖于我们设计的面部动作单元强度和可解释的上半身特征、时间分割和检测网络，以及对比和自我监督学习损失来检测抽动，而不需要大量注释。，我们将设计一种名为 Tic-DETR 的新型时空 Transformer 架构，用于细粒度抽动检测，它可以捕获跨多个视图的面部动作单元和/或骨骼关节之间的远程交互以及之间的关系最后，我们将比较我们的算法和人类实践伙伴之间的检测结果，评估不同观点的算法的稳健性，并评估其临床可解释性。