To develop an automated method of grading fluorescein staining that accurately
reproduces the clinical grading system currently in use.
METHODS. From the slit lamp photograph of the fluorescein-stained cornea, the region of
interest was selected and punctate dot number calculated using software developed with the
OpenCV computer vision library. Images (n ¼ 229) were then divided into six incremental
severity categories based on computed scores. The final selection of 54 photographs
represented the full range of scores: nine images from each of six categories. These were then
evaluated by three investigators using a clinical 0 to 4 corneal staining scale. Pearson
correlations were calculated to compare investigator scores, and mean investigator and
automated scores. Lin’s Concordance Correlation Coefficients (CCC) and Bland-Altman plots
were used to assess agreement between methods and between investigators.
Pearson’s correlation between investigators was 0.914; mean CCC between
investigators was 0.882. Bland-Altman analysis indicated that scores assessed by investigator
3 were significantly higher than those of investigators 1 and 2 (paired t-test). The predicted
grade was calculated to be: Gpred ¼ 1.48log(Ndots) 0.206. The two-point Pearson’s
correlation coefficient between the methods was 0.927 (P < 0.0001). The CCC between
predicted automated score Gpred and mean investigator score was 0.929, 95% confidence
interval (0.884–0.957). Bland-Altman analysis did not indicate bias. The difference in SD
between clinical and automated methods was 0.398.
An objective, automated analysis of corneal staining provides a quality assurance
tool to be used to substantiate clinical grading of key corneal staining endpoints in
multicentered clinical trials of dry eye.
