Automatic measurement of mouse visual acuity based on optomotor response: SKY optomotry

Autor: So Yeon Ahn, Eun Hye Jung, Hyunmin Ahn, Jihei Sara Lee, Jeong Hyeon Bak, Eun-do Kim, Ja-Hyun Song, Hae-Sol Shin, Munkhdelger Jamiyansharav, Kyoung Yul Seo
Rok vydání: 2023
Předmět:
Zdroj: Laboratory Animals. :002367722211485
ISSN: 1758-1117
0023-6772
DOI: 10.1177/00236772221148576
Popis: In the field of visual science study using rodents, several assessment methods have been developed for measuring visual function. However, methods such as electroretinograms tests, visual evoked potentials tests and maze tests have limitations in that they measure function of only a specific type of cells, are difficult to quantify or require sufficient training time. The method which uses an optokinetic reflex and optomotor response, a compensatory eye and head movement in response to changes in the visual scene, became the most widely used method. However, this method requires highly trained experimenters and is time consuming. We showed that measured visual acuity values are significantly different between beginner and expert. Here we suggest an automated optometry program, ‘SKY optomotry’, which automatically tracks rodents’ optomotor response to overcome subjectivity and the lengthy scoring procedure of the existing method. To evaluate the performance of SKY optomotry using 8–12-week-old C57BL/6 mice we compared the binomial decision of SKY optomotry with a skilled expert, and the area under the curve of SKY optomotry was 0.845. Comparing the final visual acuity, the intraclass correlation coefficient value between SKY optomotry and an expert was 0.860 (95% confidence interval (CI) 0.709–0.928), whereas that between an expert and a beginner was 0.642 (95% CI 0.292–0.811). SKY optomotry showed an excellent level of performance with good inter-rater agreements based on the visual acuity measured by an expert. With the use of our application, researchers will be able to test an experimental animal's eyesight more accurately while saving time on specialized training.
Databáze: OpenAIRE
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