Introduction: Developments in image processing techniques and display technology have led to the emergence of augmented reality (AR) and virtual reality (VR)-based low vision devices (LVDs). However, their promise and limitations in low vision rehabilitation are poorly understood. The objective of this systematic review is to appraise the application of AR/VR LVDs aimed at visual field expansion and visual acuity improvement in low vision rehabilitation.
Methods: A systematic search of the literature was performed using MEDLINE, Embase, PsychInfo, HealthStar, and National Library of Medicine (PubMed) from inception to March 6, 2022. Articles were eligible if they included an AR or VR LVD tested on a sample of individuals with low vision and provided visual outcomes such as visual acuity, visual fields, and object recognition.
Results: Of the 652 articles identified, 16 studies comprising 382 individuals with a mean age of 52.17 (SD = 18.30) years, and with heterogeneous low vision etiologies (i.e., glaucoma, age-related macular degeneration, retinitis pigmentosa) were included in this systematic review. Most articles used AR (53%), VR (40%), and one article used both AR and VR. The main visual outcomes evaluated were visual fields (67%), visual acuity (65%), and contrast sensitivity (27%). Various visual enhancement techniques were employed including variable magnification using digital zoom (67%), contrast enhancements (53%), and minification (27%). AR LVDs were reported to expand the visual field from threefold to ninefold. On average, individuals using AR/VR LVDs experienced an improved in visual acuity from 0.9 to 0.2 logMAR. Ten articles were classified as high or moderate risk of bias.
Conclusion: AR/VR LVDs were found to afford visual field expansion and visual acuity improvement in low vision populations. Even though the results of this review are promising, the lack of controlled studies with well-defined populations, use of small, convenience samples, and incomplete reporting of inclusion and exclusion criteria among included studies makes it challenging to judge the true impact of these devices. Future studies should address these limitations and compare various AR/LVDs to determine what is the ideal LVD type and vision enhancement combination based on the user's level of visual ability and lifestyle.
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http://dx.doi.org/10.1007/s00417-022-05972-4 | DOI Listing |
Clin Exp Nephrol
December 2024
The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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December 2024
College of Nursing, University of Utah, 10 South 2000 East, Salt Lake City, UT, 84112, USA.
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December 2024
Anhui Engineering Research Center for Coal Clean Processing and Carbon Reduction, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China.
Machine vision was utilized in this study to accurately classify the low concentration slurry. Orthogonal experiment L(3) indicated that the optimal coal slurry collection images were achieved with exposure value of 10, slurry layer thickness of 7 cm, and light intensity of 5 × 10 lux. Subsequently, a new low concentration classification model was systematically developed, encompassing aspects such as original image acquisition, data augmentation, dataset partitioning, classification algorithm design, and model evaluation.
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December 2024
School of Electronic Information and Automation, Tianjin, China.
Vision transformers have garnered substantial attention and attained impressive performance in image super-resolution tasks. Nevertheless, these networks face challenges associated with attention complexity and the effective capture of intricate, fine-grained details within images. These hurdles impede the efficient and scalable deployment of transformer models for image super-resolution tasks in real-world applications.
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