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使用虚拟现实技术测量超低视力个体的视觉信息获取能力。

Measuring visual information gathering in individuals with ultra low vision using virtual reality.

机构信息

Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

出版信息

Sci Rep. 2023 Feb 23;13(1):3143. doi: 10.1038/s41598-023-30249-z.

Abstract

People with ULV (visual acuity ≤ 20/1600 or 1.9 logMAR) lack form vision but have rudimentary levels of vision that can be used for a range of activities in daily life. However, current clinical tests are designed to assess form vision and do not provide information about the range of visually guided activities that can be performed in daily life using ULV. This is important to know given the growing number of clinical trials that recruit individuals with ULV (e.g., gene therapy, stem cell therapy) or restore vision to the ULV range in the blind (visual prosthesis). In this study, we develop a set of 19 activities (items) in virtual reality involving spatial localization/detection, motion detection, and direction of motion that can be used to assess visual performance in people with ULV. We estimated measures of item difficulty and person ability on a relative d prime (d') axis using a signal detection theory based analysis for latent variables. The items represented a range of difficulty levels (- 1.09 to 0.39 in relative d') in a heterogeneous group of individuals with ULV (- 0.74 to 2.2 in relative d') showing the instrument's utility as an outcome measure in clinical trials.

摘要

具有低视力(视力≤20/1600 或 1.9 logMAR)的人缺乏形态视力,但具有基本的视力水平,可用于日常生活中的各种活动。然而,目前的临床测试旨在评估形态视力,而不提供有关使用低视力可以在日常生活中进行的各种视觉引导活动的信息。鉴于越来越多的临床试验招募低视力个体(例如,基因治疗、干细胞治疗)或在盲人中恢复低视力范围的视力(视觉假体),这一点很重要。在这项研究中,我们在虚拟现实中开发了一组 19 项活动(项目),涉及空间定位/检测、运动检测和运动方向,可用于评估低视力个体的视觉表现。我们使用基于信号检测理论的潜在变量分析,在相对 d prime(d')轴上估计项目难度和个体能力的度量。这些项目在一组具有低视力的异质个体中代表了一系列难度水平(相对 d'为-1.09 到 0.39),在相对 d'为-0.74 到 2.2 的个体中表现出该工具在临床试验中的作为结果测量的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbee/9950080/225307d5cf41/41598_2023_30249_Fig1_HTML.jpg

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