Abstract
Purpose :
The mouse is particularly useful for investigating ocular development because of the large array of genetic tools available in this species. However, in vivo measurements of ocular dimensions are challenging due to the eye small size, and typically yield poor reliability and large standard deviations (SD). The purpose of this study was to assess the repeatability of measuring ocular biometry using a Bioptigen (Leica Microsystems) spectral-domain optical coherence tomography (SD-OCT) in wild-type mice.
Methods :
Ocular biometry was performed three times for four mice (34-54 days old), measurements separated by one week. Images captured with the SD-OCT were analyzed three times using programs written in MATLAB (Mathworks) in order to obtain central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT), vitreous chamber depth and retinal thickness (VCD+RT), and axial length (AL) measurements. To determine repeatability of the image analysis, the three analyses for each session were averaged, and SD and coefficient of variance (CV) determined, followed by each statistic being averaged between sessions and across all eyes. To determine intercession repeatability, the means of the three analyses were used to calculate averages, SD, and CV across sessions, followed by averaging each statistic across all eyes.
Results :
Ocular biometry measurements were: corneal thickness (0.0668 ± 0.0030mm), ACD (0.2789 ± 0.0073mm), LT (1.8005 ± 0.0804mm), VCD+RT (0.7854 ± 0.0381mm), and AL (2.9348 ± 0.0995mm). Intercession repeatability of AL measurements (shown in Figure 1) showed a SD of 0.0717mm and a low CV of 2.4587%. Repeatability of image analysis was even more precise, with the AL having a SD of 0.0088mm and CV of 0.3285%.
Conclusions :
Our OCT biometry measurements and subsequent image analysis showed high repeatability in wild-type mice. Increased reliability will be useful to further ocular development studies that aim to measure ocular biometry in various genetic knock-out mouse models.
This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.