Superficial Punctate Keratitis Grading for Dry Eye Screening Using Deep Convolutional Neural Networks
Superficial Punctate Keratitis Grading for Dry Eye Screening Using Deep Convolutional Neural Networks
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DOI：
10.1109/jsen.2019.2948576
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发表时间：
2020-02-01
影响因子：
4.3
通讯作者：
Chen, Duan-Yu
中科院分区：
文献类型：
--
作者：
Su, Tai-Yuan;Ting, Peng-Jen;Chen, Duan-Yu
眼表损害是干眼综合征的主要特征。干眼症引起的眼表损害是指眼表出现浅表性点状角膜炎（SPK），又称点状角膜炎。在目前的诊断方法中，如眼表荧光素染色试验，眼科医生将眼表染色，使点状斑点可视化，然后进行识别和计数，以进行分级。根据分级结果，眼科医生进行进一步诊断。对于专家来说，很难获得一致的结果。方法：本研究提出训练深度CNN（卷积神经网络）模型来自动检测点状点。然后，我们获得一个值，称为CNN-SPK值，它表示点状点的覆盖率。收集了101名参与者的标准荧光染色图像。结果如下：其余81例患者CNN-SPK估计值与临床分级相关（r = 0.85; p < 0.05）。基于这一观察，我们建议一个统计方法的最终分级。使用来自81名参与者的CNN-SPK值以及相应的临床分级，我们发现任何两个相邻等级之间的CNN-SPK阈值。此外，我们获得了有和没有SPK症状之间的阈值，灵敏度为0.94，特异性为0.79。结论：自动化方法可以对点状出血的严重程度进行可靠的分级，提高了干性诊断的效率。
Ocular surface damage is a major characteristic of dry eye syndrome. Ocular surface damage caused from dry eye refers to that there is superficial punctate keratitis (SPK), or also called the punctate dots, on the ocular surface. In the current diagnostic methods such as the ocular surface fluorescein-staining test, ophthalmologists dye the ocular surface to visualize punctate dots and then identify as well as count them for grading. Based on the grading results, ophthalmologists conduct a further diagnosis. For an expert, it is hard to achieve consistent results. Method: This study proposed to train a deep CNN (convolutional neural network) model to automatically detect punctate dots. Then we obtain a value, called the CNN-SPK value, which represents the coverage of punctate dots. Standard fluorescein-staining images from 101 participants were collected. Results: The correlation between the estimated CNN-SPK values of the rest 81 participants and the clinical grades were significant (r = 0.85; p < 0.05). Based on this observation, we suggest a statistical approach for the final grading. Using CNN-SPK values from 81 participants, as well as the corresponding clinical grades, we find CNN-SPK thresholds between any two adjacent grades. Also, we obtain the threshold between with- and without- SPK-symptoms, leading to 0.94 in sensitivity, and 0.79 in specificity. Conclusion: Our automatic method may be used to reliably grade the severity of punctate dots, to improve the efficiency of the dry diagnosis.