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 = 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 = 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.