The feasibility of clinical evaluation for anterior uveitis through spectral-domain optical coherence tomography in dogs.

2021 
OBJECTIVE To evaluate the clinical application of spectral-domain optical coherence tomography (SD-OCT) for anterior uveitis in dogs. ANIMALS AND PROCEDURES Client-owned dogs presenting with anterior uveitis and clinically healthy dogs were enrolled in this study. Included eyes were divided into 5 groups by flare grade and 3 groups by cell grade through slit-lamp biomicroscopy. Each eye was examined using SD-OCT following slit-lamp biomicroscopy. The ratio of aqueous signal intensity to air signal intensity, which is called the aqueous-to-air relative intensity (ARI) index, was used to evaluate the flare grade. Cell number, central corneal thickness (CCT), and the presence of keratic precipitates (KPs) were analyzed on SD-OCT. The OCT parameters, including ARI index, cell number, and CCT, were compared to the slit-lamp clinical flare and cell grade. RESULTS Thirty-six eyes with anterior uveitis and 27 healthy eyes were enrolled. The ARI index showed a significant correlation with clinical flare grade (rs  = 0.811, p < .001). In multiple regression analysis, the ARI index and CCT showed a significant negative correlation (r = -0.258, p = .044). The number of cells on SD-OCT significantly increased with cell grade on slit-lamp biomicroscopy (rs  = 0.653, p < .001). The clinical flare grade and CCT were significantly correlated in the partial correlation analysis after controlling for age (partial correlation coefficient = 0.471, p = .002). KPs were observed in 61% of the eyes with flare using SD-OCT (22/36 eyes). CONCLUSIONS Spectral-domain optical coherence tomography could provide quantitative information, including the ARI index, cell counts, and CCT in dogs. SD-OCT is an auxiliary modality for slit-lamp biomicroscopy when evaluating anterior uveitis in dogs.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    32
    References
    0
    Citations
    NaN
    KQI
    []