Emulation of Physician Tasks in Eye-tracked Virtual Reality for Remote Diagnosis of Neurodegenerative Disease
Emulation of Physician Tasks in Eye-tracked Virtual Reality for Remote Diagnosis of Neurodegenerative Disease
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DOI：
10.1109/tvcg.2017.2657018
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发表时间：
2017-04-01
影响因子：
5.2
通讯作者：
Devos, Hannes
中科院分区：
文献类型：
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作者：
Orlosky, Jason;Itoh, Yuta;Devos, Hannes
对于帕金森病等神经退行性疾病，早期准确诊断仍然是一项艰巨的任务。评估可能非常耗时，患者必须经常前往大都市地区或不同城市去看专家，误诊可能会导致治疗不当。迄今为止，只有少数辅助或远程方法可以帮助医生以方便且一致的方式评估疑似神经系统疾病的患者。在本文中，我们提出了一种低成本的 VR 界面，旨在支持神经退行性疾病的评估和诊断，并测试其在临床环境中的使用。我们使用市售的 VR 显示器以及集成在镜头中的红外摄像头构建了一个 3D 虚拟环境，旨在模拟用于评估患者的常见任务，例如注视某个点、平滑地追踪某个物体或执行眼跳。这些虚拟任务旨在引发通常与神经退行性疾病相关的眼球运动，例如异常眼跳、方波抽搐和眼震。接下来，我们对 9 名被诊断为帕金森病的患者和 7 名健康对照者进行了实验，以测试该系统模拟临床诊断任务的潜力。然后，我们将眼动追踪算法和图像增强应用于实验期间拍摄的眼部记录，并与两名医生进行了简短的后续研究以进行评估。结果显示，我们的 VR 界面能够引出五种常见的可用于评估的运动类型，医生能够确认四种异常中的三种，并且可视化被认为对诊断可能有用。
For neurodegenerative conditions like Parkinson's disease, early and accurate diagnosis is still a difficult task. Evaluations can be time consuming, patients must often travel to metropolitan areas or different cities to see experts, and misdiagnosis can result in improper treatment. To date, only a handful of assistive or remote methods exist to help physicians evaluate patients with suspected neurological disease in a convenient and consistent way. In this paper, we present a low-cost VR interface designed to support evaluation and diagnosis of neurodegenerative disease and test its use in a clinical setting. Using a commercially available VR display with an infrared camera integrated into the lens, we have constructed a 3D virtual environment designed to emulate common tasks used to evaluate patients, such as fixating on a point, conducting smooth pursuit of an object, or executing saccades. These virtual tasks are designed to elicit eye movements commonly associated with neurodegenerative disease, such as abnormal saccades, square wave jerks, and ocular tremor. Next, we conducted experiments with 9 patients with a diagnosis of Parkinson's disease and 7 healthy controls to test the system's potential to emulate tasks for clinical diagnosis. We then applied eye tracking algorithms and image enhancement to the eye recordings taken during the experiment and conducted a short follow-up study with two physicians for evaluation. Results showed that our VR interface was able to elicit five common types of movements usable for evaluation, physicians were able to confirm three out of four abnormalities, and visualizations were rated as potentially useful for diagnosis.