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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
Published on: February 23, 2024
A new measure of nystagmus acuity
Jun-Ping Yao1, Zheng Tai2, Zheng-Qin Yin3
1Department of Ophthalmology, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan 430036, Hubei Province, China ; Chongqing Key Lab of Visual Damage and Regeneration & Restoration, Chongqing 400038, China.
Aim:
To construct a new visual acuity measuring function for congenital nystagmus (CN) patients by studying the relationships between acuity, velocities and positions of the eye.
Methods:
After assessing the relationship between acuity, movement velocities and positions of the eye separately, a new function, which we call the automated nystagmus acuity function (ANAF), was constructed to measure the visual acuity of CN patients. Using a high-speed digital video system working at 500 frames per second, each eye was calibrated during monocular fixation. Twenty-six recorded nystagmus data were selected randomly. Using nystagmus waveforms, the best vision position (foveation period) and visual acuity were analyzed in three groups of subjects, and then all outputs were compared with the well-known expanded nystagmus acuity function (NAFX) and ANAF. Standard descriptive statistics were used to summarize the outputs of the two programs.
Results:
Foveation periods were brief intervals in the CN waveform when the image was on or near the fovea and eye velocity was relatively slow. Results showed good visual acuity happened during the period when velocity was low and the eye position was near the zero position, which fitted the foveation periods. The data analyzed with NAFX and ANAF had a correlation coefficient of 0.934276, with an average error of -0.00973.
Conclusion:
The results from ANAF and NAFX analyses showed no significant difference. The NAFX manually identifies foveation eye positions and produces accurate measurements. The ANAF, however, can be calculated simply using the factors eye position and velocity, and it automatically calculates the ANAF without the need to manually identify foveation eye positions.
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