Objective: Differentiating benign from dangerous causes of dizziness or vertigo presents a major diagnostic challenge for many clinicians. Bedside presentations of peripheral vestibular disorders and posterior fossa strokes are often indistinguishable other than by a few subtle vestibular eye movements. The most challenging of these to interpret is the head impulse test (HIT) of vestibulo-ocular reflex (VOR) function. There have been major advances in portable video-oculography (VOG) quantification of the video HIT (vHIT), but these specialized devices are not routinely available in most clinical settings. As a first step towards smartphone-based diagnosis of strokes in patients presenting vestibular symptoms, we sought proof of concept that we could use a smartphone application (“app”) to accurately record the vHIT. Methods: This was a cross-sectional agreement study comparing a novel index test (smartphone-based vHIT app) to an accepted reference standard test (VOG-based vHIT) for measuring VOR function. We recorded passive (examiner-performed) vHIT sequentially with both methods in a convenience sample of patients visiting an otoneurology clinic. We quantitatively correlated VOR gains (ratio of eye to head movements during the HIT) from each side/ear and experts qualitatively assessed the physiologic traces by the two methods. Results: We recruited 11 patients; 1 patient’s vHIT could not be reliably quantified with either device. The novel and reference test VOR gain measurements for each ear (n = 20) were highly correlated (Pearson’s r = 0.9, p = 0.0000001) and, qualitatively, clinically equivalent. Conclusions: This preliminary study provides proof of concept that an “eyePhone” app could be used to measure vHIT and eventually developed to diagnose vestibular strokes by smartphone.
Digit Biomark 2021;5:1–8

中文翻译：

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用于测量视频头部脉冲的“eyePhone”应用程序的概念验证

目的：区分良性和危险的头晕或眩晕原因对许多临床医生来说是一个重大的诊断挑战。除了一些细微的前庭眼球运动外，外周前庭疾病和后颅窝中风的床边表现通常难以区分。其中最具挑战性的是前庭眼反射 (VOR) 功能的头部脉冲测试 (HIT)。在视频 HIT (vHIT) 的便携式视频眼科 (VOG) 量化方面取得了重大进展，但这些专用设备在大多数临床环境中并不常见。作为对出现前庭症状的患者进行基于智能手机的中风诊断的第一步，我们寻求概念证明，即我们可以使用智能手机应用程序（“应用程序”）准确记录 vHIT。方法：这是一项横断面一致性研究，比较了用于测量 VOR 功能的新型指数测试（基于智能手机的 vHIT 应用程序）与公认的参考标准测试（基于 VOG 的 vHIT）。我们在访问耳科诊所的患者的便利样本中使用两种方法顺序记录被动（检查者执行）vHIT。我们定量地关联每侧/耳朵的 VOR 增益（HIT 期间眼睛与头部运动的比率），专家通过这两种方法定性评估生理痕迹。结果：我们招募了 11 名患者；使用任何一种设备都无法可靠地量化 1 名患者的 vHIT。每只耳朵（ n = 20）的新颖和参考测试 VOR 增益测量值高度相关（Pearson 的r = 0.9，p = 0.0000001），并且在质量上，临床等效。结论：这项初步研究提供了概念证明，即“eyePhone”应用程序可用于测量 vHIT，并最终开发用于通过智能手机诊断前庭中风。
数字生物标志 2021；5:1–8