Department of Ophthalmology, Peking University Third Hospital, Beijing, China.
Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China.
Br J Ophthalmol. 2023 Apr;107(4):453-460. doi: 10.1136/bjophthalmol-2021-319755. Epub 2021 Oct 20.
The goal was to develop a fully automated grading system for the evaluation of punctate epithelial erosions (PEEs) using deep neural networks.
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.
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.
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.
旨在开发一种使用深度神经网络自动评估点状上皮糜烂（PEEs）的分级系统。
开发了一种全自动系统，用于在获得角膜荧光素染色图像后检测角膜位置并对染色严重程度进行分级。全自动流水线由以下三个步骤组成：角膜分割模型提取角膜区域；根据提取的角膜的五个子区域，从染色图像中裁剪五个图像块；染色分级模型对每个图像块从 0 到 3 预测一个分数，从 0 到 15 获得整个角膜的自动分级分数。最后，将三位眼科医生标注的临床分级分数与自动分级分数进行比较。
对于角膜分割，分割模型的交并比为 0.937。对于点状染色分级，分级模型的分类准确率为 76.5%，受试者工作特征曲线下面积为 0.940（95%CI 0.932 至 0.949）。对于全自动流水线，临床和自动分级分数之间的 Pearson 相关系数为 0.908（p<0.01）。Bland-Altman 分析显示临床和自动分级分数之间的 95%一致性界限为-4.125 至 3.720（一致性相关系数=0.904）。在流水线中处理单个染色图像的平均时间为 0.58 秒。
开发了一种全自动分级系统来评估 PEEs。分级结果可以作为眼科医生在临床试验和住院医师培训程序中的参考。
