论文标题
基于虚拟现实的前庭眼运动筛选，用于使用机器学习的脑震荡检测
Virtual-Reality based Vestibular Ocular Motor Screening for Concussion Detection using Machine-Learning
论文作者
论文摘要
与运动有关的脑震荡（SRC）取决于视觉，前庭和体感系统中的感觉信息。同时，目前的前庭/眼运动筛查（VOM）临床给药是主观的，并且管理员之间存在偏差。因此，为了评估和管理脑震荡检测，需要标准化以降低受伤的风险并增加临床医生的验证。随着技术的发展，可以利用虚拟现实（VR）来提高VOM的标准化，从而提高测试管理的准确性并降低总体假阳性率。在本文中，我们尝试了多种机器学习方法，以使用VOM在VR生成的数据上检测SRC。在我们的观察中，从VR生成的平滑追击（SP）和视觉运动灵敏度（VM）测试的数据对于脑震荡检测非常可靠。此外，我们在定性和定量上训练和评估这些模型。我们的发现表明，这些模型可以达到基于VR刺激的VOM与当前临床手册VOM的症状挑衅的高度阳性率。
Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. At the same time, the current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. Therefore, for the assessment and management of concussion detection, standardization is required to lower the risk of injury and increase the validation among clinicians. With the advancement of technology, virtual reality (VR) can be utilized to advance the standardization of the VOMS, increasing the accuracy of testing administration and decreasing overall false positive rates. In this paper, we experimented with multiple machine learning methods to detect SRC on VR-generated data using VOMS. In our observation, the data generated from VR for smooth pursuit (SP) and the Visual Motion Sensitivity (VMS) tests are highly reliable for concussion detection. Furthermore, we train and evaluate these models, both qualitatively and quantitatively. Our findings show these models can reach high true-positive-rates of around 99.9 percent of symptom provocation on the VR stimuli-based VOMS vs. current clinical manual VOMS.
