Poster Session: Comparison of Dynamic Visual Acuity Assessments in Head-Mounted Technology and Traditional Laptop-based Method
Year: 2023
Type: article
Abstract: An impairment in dynamic visual acuity (DVA) has been observed in astronauts shortly after they return to Earth.1 These transitional effects may lead to safety risks during interplanetary spaceflight. At this time, functional vision assessments are performed via laptop onboard the International Space Station. However, DVA is not performed as a standard assessment, and optimization of traditional assessments may aid in more efficient and frequent testing. As part of our group's NASA-funded head-mounted visual assessment system to detect subtle vision changes in long-duration spaceflight2, we present a method to measure DVA in virtual reality. An early validation study was conducted with 5 subjects comparing our novel assessment with a traditional laptop-based test. All participants had a best correctable visual acuity of 20/20, had no past ocular history, balancing disorders, or neurological history. Our DVA assessment framework was built in UnrealEngine 4. The early validation study confirmed that our VR-based DVA assessment performed similarly to traditional laptop-based test (0.485 and 0.525 LogMar respectively, Pearson Correlation = 0.911). A Bland-Altman plot and analysis demonstrated that our DVA assessment data fell within the upper and lower limits of agreement. Future studies are required to further validate this technology; however, these early results showcase VR-based DVA assessment as a promising alternative to laptop-based methods.
Source: Journal of Vision
DOI: 10.1167/jov.23.11.80
Institutions: University of Nevada, Reno, University College Dublin, University of Michigan, Michigan Medicine, Brigham and Women's Hospital, Harvard University, University of Iowa, Houston Methodist, The University of Texas MD Anderson Cancer Center, University of Iowa Hospitals and Clinics, Weill Cornell Medicine, The University of Texas Medical Branch at Galveston
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,850
OpenAlex ID: W4386923600
