Automated Grading System for Evaluation of Superficial Punctate Keratitis Associated With Dry Eye

PURPOSE.

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.

RESULTS.

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.

CONCLUSIONS.

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.