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. Redvalues 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.