Development and Evaluation of Semiautomated Quantification of Lissamine Green Staining of the Bulbar Conjunctiva From Digital Images

IMPORTANCE Lissamine green (LG) staining of the conjunctiva is a key biomarker in evaluating ocular surface disease. The disease currently is assessed using relatively coarse subjective scales. Objective assessment would standardize comparisons over time and between clinicians. OBJECTIVE To develop a semiautomated, quantitative system to assess lissamine green staining of the bulbar conjunctiva on digital images. DESIGN, SETTING, AND PARTICIPANTS Using a standard photography protocol, 35 digital images of the conjunctiva of 11 patients with a diagnosis of dry eye disease based on characteristic signs and symptoms were obtained after topical administration of preservative-free LG, 1%, solution. Images were scored independently by 2 masked ophthalmologists in an academic medical center using the van Bijsterveld and National Eye Institute (NEI) scales. The region of interest was identified by manually marking 7 anatomic landmarks on the images. An objective measure was developed by segmenting the images, forming a vector of key attributes, and then performing a random forest regression. Subjective scores were correlated with the output from a computer algorithm using a cross-validation technique. The ranking of images from least to most staining was compared between the algorithm and the ophthalmologists. The study was conducted from April 26, 2012, through June 2, 2016. MAIN OUTCOMES AND MEASURES Correlation and level of agreement among computerized algorithm scores, van Bijsterveld scale clinical scores, and NEI scale clinical scores. RESULTS The scores from the automated algorithm correlated well with the mean scores obtained from the gradings of 2 ophthalmologists for the 35 images using the van Bijsterveld scale (Spearman correlation coefficient, r(s) = 0.79), and moderately with the NEI scale (r(s) = 0.61) scores. For qualitative ranking of staining, the correlation between the automated algorithm and the 2 ophthalmologists was r(s) = 0.78 and r(s) = 0.83. CONCLUSIONS AND RELEVANCE The algorithm performed well when evaluating LG staining of the conjunctiva, as evidenced by good correlation with subjective gradings using 2 different grading scales. Future longitudinal studies are needed to assess the responsiveness of the algorithm to change of conjunctival staining over time.