目的：设计一套瞳孔动、静态信息提取方案，尝试初步建立正常人群瞳孔动、静态信息数据库。方法：于2023年1月—7月，从中山大学眼科中心眼科门诊招募无眼部疾病、全身疾病的参与者。使用工业级红外相机搭配850 nm红外光源录制参与者瞳孔区域20 s视频，提取瞳孔横径数据并输出为txt文件。使用R语言分析数据，构建瞳孔舒缩过程中的波峰、波谷拟合线，并估算瞳孔舒缩频率。结果：共收集32位参与者的瞳孔动态数据，右眼数据纳入主要结果的分析。参与者年龄范围为7~61岁，男性占50%，等效球镜度范围为-5.630~+5.00 D。测量过程中的瞳孔横径平均值与年龄呈负相关，与等效球镜度、性别无关。在20 s的记录中，瞳孔舒缩次数为(15±7)次。基于波峰与波谷拟合线的趋势，瞳孔舒缩动态分为3种类型：舒缩扩张型、舒缩稳定型和舒缩缩小型。舒缩稳定型进一步分为稳定扩张型、稳定恒定型和稳定缩小型3个亚型，其中25人为舒缩缩小型。双眼的过滤后波峰、波谷拟合线的斜率和截距与过滤前比较差异无统计学意义(P>0.05)。结论：低成本、简易的设备配以算法可以便捷、快速地提取瞳孔动、静态信息。
Objective: To design a method for extracting dynamic and static information of the pupil and to establish a database of pupil dynamic and static information in a healthy population. Methods: From January to July 2023, subjects without any ocular or systemic diseases were recruited from the ophthalmology outpatient department. An industrial-grade infrared camera, paired with an 850nm infrared light source, was used to record 20-second videos of the pupil area of each subject. Horizontal pupil diameter data was extracted and saved as txt files. The data was analyzed using R software to construct fitted lines of peaks and troughs during the pupil constriction and dilation process, and the frequency of pupil contraction and dilation was estimated. Results: Pupil dynamic data was collected from 32 subjects with an age range of 7 to 61 years, of whom 50% were male. The spherical equivalent range was from +5.00 D to -5.625 D. The average number of pupil contractions and dilations within the 20-second recordings was (15 ± 7) cycles. Based on the trend of fitted lines for peaks and troughs, pupil dynamic types were categorized into three types: dilation type, stable type, and contraction type. The stable type was further divided into stable dilation, stable constant, and stable contraction subtypes. 25 subjects exhibiting the constriction type. A paired t-test showed no significant difference in the slope and intercept of the fitted lines for peaks and troughs between both eyes. Conclusion: Low-cost and simple equipment combined with algorithms can efficiently and quickly extract dynamic and static pupil information.
青光眼是全世界范围内不容忽视的致盲性眼病，其起病隐匿，视功能损害进展迅速，晚期预后不理想。长期发展且未行治疗的青光眼患者视神经呈进行性损害，引起视力急剧下降、视野不可逆性缺损，严重降低患者的生活质量。为了适应逐渐恶化的视功能，患者注视行为发生明显变化，由此在日常活动中引起步态行为随之改变，意外事故频繁发生。故目前对于青光眼的研究引起国内外广泛重视，虚拟现实技术(virtual reality，VR)作为青光眼早期诊断及康复治疗的新手段已被现代医学所尝试。本文具体阐述了青光眼的视觉损害与注视行为及运动行为间的联系，并总结了目前国内外关于VR诊断青光眼及作为康复治疗的相关研究。
Glaucoma is a blinding eye disease that cannot be ignored worldwide. Its onset is insidious, visual impairment is progressing rapidly, and the late prognosis is not ideal. Long-term untreated glaucoma patients show progressive damage to the optic nerve, causing a sharp decline in vision, irreversible visual field defects, and severely reducing the quality of life of the patients. In order to adapt to the gradual deterioration of visual function, the patient’s gaze behavior changes significantly, which causes the gait behavior to change in daily activities, and accidents occur frequently. Therefore, the current research on glaucoma has attracted wide attention in the nation and abroad,and virtual reality (VR) technology has been tried in modern medicine as a new method for early diagnosis and rehabilitation of glaucoma. This article specifically elaborates the relationship between the visual impairment of glaucoma and the gaze behavior and movement behavior, and summarizes the current domestic and foreign research on the diagnosis of glaucoma and the rehabilitation of VR technology.
