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

Abstract

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 MEASURESCorrelation and level of agreement among computerized algorithm scores, van Bijsterveld scale clinical scores, and NEI scale clinical scores. RESULTSThe 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 RELEVANCEThe 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.

Multiscale reverse engineering of the human ocular surface

“…After 1-2 min, the cornea was then examined with the slit lamp at ×10 magnification using the cobalt blue light source. Fluorescein staining of the cornea was graded using the van Bijsterveld scale 58 .…”

Automation of dry eye disease quantitative assessment: A review

“…The method resulted in a strong correlation with the Oxford scale and a better correlation with the National Eye Institute/Industry grading scale than both methods by References 62 and 63, which were similar. Another automated method of staining detection that uses image processing was developed by Rodriguez et al 56 The binary images are used for blob detection, and those are the punctate dots.…”

“…Another study using lissamine green dye by Bunya et al evaluates ocular surface damage only to the bulbar conjunctiva. 63 The image is then processed and a random forest regression classifier is trained using the extracted features. The method uses automated acquisition and grading but requires user assistance for the region of interest selection.…”

“…It also has a better interobserver agreement when compared to that of the National Eye Institute/Industry grading scale. Another study using lissamine green dye by Bunya et al evaluates ocular surface damage only to the bulbar conjunctiva 63 . The image is then processed and a random forest regression classifier is trained using the extracted features.…”

Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks

“…6 Computer-aided diagnosis has been applied to PEEs, and many semiautomated grading systems for PEEs have been developed. [7][8][9] However, these systems still require manual assistance.…”