Assessing ocular bulbar redness: a comparison of methods

Purpose: We consider whether quantification of ocular bulbar redness, using image processing of relative Red-channel activity (Red-value), can be applied to a clinical sample and how this approach compares to an automated bulbar redness grading technique (Oculus Keratograph 5M, R-scan).

Methods: Red-values from dry eye patients (n = 25) were determined using image processing of digital photographs over the nasal bulbar conjunctiva. Red-values were compared with subjective grades from six clinicians who graded the images using the IER scale. We considered the level of agreement between the Red-value and automated bulbar redness scores from the commercial instrument (R-scan). Scoring variability for each technique was assessed using the geometric coefficient of variation (gCoV, %). Agreement between techniques was considered with Bland-Altman analyses.

Results: Red-values showed a strong linear relationship (R(2) = 0.99) to the R-scan. The Red-value had least variability (gCoV = 0.97%, 95% CI: 0.76-1.35%). The IER grade showed a linear relationship with Red-value (R(2) = 0.99), bound by a floor effect; it did not discriminate changes in redness below a threshold of 1.75 units (Red-value = 33.0%), after which it paralleled the redness returned by the R-scan. Intra-method variability for the redness returned by the R-scan (gCoV = 9.84%, 95% CI: 7.60-13.94%) and IER grades (gCoV = 7.30%, 95% CI: 1.73-10.31%) was similar (p > 0.05). Bland-Altman analysis showed the R-scan was consistently biased towards lower absolute redness scores than the IER.

Conclusions: Digital imaging processing, using relative Red-channel activity, was the least variable of the three techniques. The R-scan and IER showed similar intra-observer variability. The linear relationship between R-scan and Red-value suggests that the R-scan could be derived using similar methods.