Clinical Research Directory

Measuring Relative Afferent Pupillary Defect

Sponsor: The University of Texas Medical Branch, Galveston

Summary

The goal of this prospective reliability study is to test the effectiveness of a commercially available, off-the-shelf virtual reality head-mounted display (VR HMD) and machine learning (ML) algorithms in detecting Relative Afferent Pupillary Defect (RAPD) in a group of subjects with known RAPD and another group with no known RAPD. The main questions it aims to answer are: * Does the use of the VR HMD and ML to replace the standard of care swinging flashlight test provide a more reliable and objective pupil measurement to detect RAPD? * Can RAPD be detected by the VR HMD and ML algorithms at an earlier stage than the standard of care swinging light test? Participants will be asked to undergo the standard of care swinging flashlight test, have their pupils manually measured, then have the test repeated using the VR HMD and ML. Researchers will compare the measurements taken manually, following the standard of care swinging light test and those recorded by the VR HMD and ML to help answer the above questions.

Official title: A Mechanism for Measurement of Relative Afferent Pupillary Defect (RAPD) Using Machine Learning

Key Details

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Interventions

Locations (1)