Automated Grading System for Evaluation of Superficial Punctate Keratitis Associated With Dry Eye
Automated Grading System for Evaluation of Superficial Punctate Keratitis Associated With Dry Eye
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DOI：
10.1167/iovs.14-15318
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发表时间：
2015-04-01
影响因子：
4.4
通讯作者：
Abelson, Mark B.
中科院分区：
文献类型：
--
作者：
Rodriguez, John D.;Lane, Keith J.;Abelson, Mark B.
目的.目的：建立一种荧光素染色分级的自动化方法，该方法能准确地再现目前使用的临床分级系统。从荧光素染色的角膜的裂隙灯照片中，选择感兴趣的区域，并使用OpenCV计算机视觉库开发的软件计算点状点数量。然后根据计算的评分将图像（n = 229）分为6个增量严重程度类别。最后选出的54张照片代表了全部得分范围：六个类别中每个类别九张照片。然后由三名研究者使用临床0 - 4角膜染色量表对这些进行评价。计算Pearson相关性以比较研究者评分以及平均研究者评分和自动评分。Lin的一致性相关系数（CCC）和Bland-Altman图用于评估方法之间和校准器之间的一致性。研究者之间的Pearson相关系数为0.914;研究者之间的平均CCC为0.882。Bland-Altman分析表明，研究者3评估的评分显著高于研究者1和2（配对t检验）。预测等级计算为：G（pred）= 1.48 log（N-dots）- 0.206。两点Pearson相关系数为0.927（P < 0.0001）。预测的自动评分Gpred和平均研究者评分之间的CCC为0.929，95%置信区间（0.884-0.957）。Bland-Altman分析未显示偏倚。临床方法与自动方法的标准差为0.398。角膜染色的客观、自动化分析提供了一种质量保证工具，可用于证实干眼多中心临床试验中关键角膜染色终点的临床分级。
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: G(pred) = 1.48log(N-dots) - 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.