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

抽动秽语综合征（TS）在儿童期发病，影响1%的人口，并造成实质性损害。健康专家建议行为疗法作为TS的一线治疗，因为它的疗效和不良反应。在行为疗法中，患者学习抽搐管理技能，并被分配“家庭作业”以巩固技能学习。核心技能包括建立对抽搐发生的意识，并实施行为策略以抑制这种意识的抽搐表达。对行为疗法表现出治疗反应的青少年继续受益10年。然而，超过50%的患者没有达到治疗反应，并依赖于FDA批准的药物，这些药物对健康有不利影响。行为疗法的一个关键挑战是，由于可访问性和准确性，依赖于人类实践伙伴进行“家庭作业”。我们的团队将创建一个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视角的抽搐。该系统将演变成一种治疗工具（即，“数字实践伙伴”），其在检测抽搐方面是可扩展的、可访问的和准确的。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患者的抽搐，并完善我们的分层本体论的抽搐注释从我们的传感器系统收集的数据。这将使临床上可解释的和细粒度的分类抽搐和非抽搐运动。其次，我们将设计一种名为Tic-Net的新型时空CNN，用于从多个视角对视频数据中的面部和上身抽搐进行细粒度检测，这将依赖于我们设计的面部动作单元强度和可解释的上身部分特征，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搐，而无需大量注释。第三，我们将设计一种新的时空Transformer架构，称为Tic-DETR，用于细粒度的抽搐检测，它捕获跨多个视图的面部动作单元和/或骨骼关节之间的长距离交互，以及抽搐实例之间的关系，以产生可解释的检测从多个视角的不同持续时间的抽搐。最后，我们将比较我们的算法和人类实践合作伙伴之间的检测结果，评估算法在不同观点之间的鲁棒性，并评估其临床可解释性。

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.