虚拟现实与预测在视野测试中的作用

Emre Bulbul, G. Akar
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摘要

视野测试是评估患者视野的黄金标准。为了监测和诊断几种疾病,包括影响8000多万人的青光眼,需要进行视野测试。当患者固定在某一位置时,将不同亮度的光发送到固定的位置,通过记录患者对观察到的刺激的反应来计算每个位置对光的敏感度。由于其设计和数字显示,虚拟现实耳机才刚刚开始用于进行视野评估。然而,由于测试时间太长,患者会感到疲劳,这降低了合作和测试的准确性。它还限制了诊所在一天内可以做的检查数量。本文使用数字屏幕扩展可测试点的位置数量,并研究了使用强化学习方法减少测试长度所发现的最优站点子集的选择效果。此外,还比较了在测试中采用预测的未来视野测试结果对测试时间的影响。
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The Effect of Virtual Reality and Prediction in Visual Field Test
Visual field testing is the gold standard for evaluating a patient’s visual field. Visual field testing is required for monitoring and diagnosis of several disorders, including glaucoma, which affects more than 80 million individuals. While the patient is fixated at a certain place, light of various luminosities is sent to fixed locations, and the sensitivities to light at each position are calculated by recording the patient’s responses to observed stimuli. Virtual reality headsets have just begun to be used to conduct visual field assessments due to their design and digital displays. However, because the testing takes so long, patients become fatigued, which reduces cooperation and test accuracy. It also restricts the number of tests a clinic may do in a single day. The number of testable point locations is expanded using a digital screen in this article, and the effect of selecting an optimal subset of sites, which is discovered using a reinforcement learning approach to reduce test length, is studied. In addition, the impact of employing predicted future visual field test results in testing on the test time is compared to traditional testing procedures.
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