Development and Evaluation of Semiautomated Quantification of Lissamine Green Staining of the Bulbar Conjunctiva From Digital Images
Development and Evaluation of Semiautomated Quantification of Lissamine Green Staining of the Bulbar Conjunctiva From Digital Images
复制标题
数字图像对球结膜丽丝胺绿染色半自动定量的开发和评估
DOI：
10.1001/jamaophthalmol.2017.3346
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发表时间：
2017-10-01
影响因子：
8.1
通讯作者：
Maguire, Maureen G.
中科院分区：
文献类型：
--
作者：
Bunya, Vatinee Y.;Chen, Min;Maguire, Maureen G.
结膜的丽丝胺绿（LG）染色是评估眼表疾病的关键生物标志物。目前对该病的评估采用相对粗糙的主观量表。客观评估将使不同时间和临床医生之间的比较标准化。目的建立一种半自动化、定量的系统来评估数字图像上球结膜的丽丝胺绿染色。设计、环境和参与者采用标准摄影方案，在局部给予不含防腐剂的1% LG溶液后，根据特征体征和症状获得11例干眼病诊断患者结膜的35张数字图像。图像由学术医疗中心的2名蒙面眼科医生使用van Bijsterveld和国家眼科研究所（NEI）量表独立评分。通过手动标记图像上的7个解剖地标来识别感兴趣的区域。通过对图像进行分割，形成关键属性向量，然后进行随机森林回归，开发了一种客观测量方法。主观得分与使用交叉验证技术的计算机算法的输出相关联。比较了算法与眼科医生对图像染色从少到多的排序。该研究于2012年4月26日至2016年6月2日进行。计算机算法评分、van Bijsterveld量表临床评分和NEI量表临床评分之间的相关性和一致性水平。结果自动算法的评分与2名眼科医生对35张图像进行van Bijsterveld评分的平均评分相关性良好（Spearman相关系数r(s) = 0.79），与NEI评分的评分相关性中等（r(s) = 0.61）。对于染色的定性排序，自动化算法与2名眼科医生的相关性r(s) = 0.78和r(s) = 0.83。结论和相关性该算法在评估结膜LG染色时表现良好，与2种不同评分标准的主观评分有良好的相关性。未来的纵向研究需要评估该算法对结膜染色随时间变化的响应性。
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.