Purpose The goal was to develop a fully automated grading system for the evaluation of punctate epithelial erosions (PEEs) using deep neural networks. Methods A fully automated system was developed to detect corneal position and grade staining severity given a corneal fluorescein staining image. The fully automated pipeline consists of the following three steps: a corneal segmentation model extracts corneal area; five image patches are cropped from the staining image based on the five subregions of extracted cornea; a staining grading model predicts a score for each image patch from 0 to 3, and automated grading score for the whole cornea is obtained from 0 to 15. Finally, the clinical grading scores annotated by three ophthalmologists were compared with automated grading scores. Results For corneal segmentation, the segmentation model achieved an intersection over union of 0.937. For punctate staining grading, the grading model achieved a classification accuracy of 76.5% and an area under the receiver operating characteristic curve of 0.940 (95% CI 0.932 to 0.949). For the fully automated pipeline, Pearson’s correlation coefficient between the clinical and automated grading scores was 0.908 (p<0.01). Bland-Altman analysis revealed 95% limits of agreement between the clinical and automated grading scores of between −4.125 and 3.720 (concordance correlation coefficient=0.904). The average time required for processing a single stained image during pipeline was 0.58 s. Conclusion A fully automated grading system was developed to evaluate PEEs. The grading results may serve as a reference for ophthalmologists in clinical trials and residency training procedures. Data are available upon reasonable request. None.

中文翻译：

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使用深度神经网络评估点状上皮侵蚀的全自动分级系统

目的我们的目标是开发一个全自动分级系统，用于使用深度神经网络评估点状上皮侵蚀 (PEE)。方法 开发了一个全自动系统，根据角膜荧光素染色图像检测角膜位置和染色严重程度。全自动流水线包括以下三个步骤：角膜分割模型提取角膜区域；基于提取角膜的五个分区，从染色图像中裁剪出五个图像块；染色分级模型预测每个图像块的分数从 0 到 3，并获得整个角膜的自动分级分数从 0 到 15。最后，将三位眼科医生注释的临床分级分数与自动分级分数进行比较。结果对于角膜分割，分割模型实现了 0.937 的并集交集。对于点状染色分级，分级模型实现了 76.5% 的分类准确度和 0.940 的受试者工作特征曲线下面积（95% CI 0.932 至 0.949）。对于全自动流程，临床评分和自动化评分之间的 Pearson 相关系数为 0.908 (p<0.01)。Bland-Altman 分析显示临床评分和自动评分之间的 95% 一致性限制在 -4.125 和 3.720 之间（一致性相关系数 = 0.904）。在管道过程中处理单个染色图像所需的平均时间为 0.58 秒。结论 开发了一个全自动分级系统来评估 PEE。分级结果可作为眼科医生在临床试验和住院医师培训程序中的参考。可根据合理要求提供数据。没有任何。