Department of Ophthalmology, Peking University Third Hospital, Beijing, China (J-H.Q, R-M.P, G-G.X, S-F.G, H-K.W, J.H); Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China (J-H.Q, R-M.P, G-G.X, S-F.G, H-K.W, J.H).
Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China (X-R.Q, J.C).
Am J Ophthalmol. 2022 Jul;239:142-153. doi: 10.1016/j.ajo.2022.02.026. Epub 2022 Mar 11.
To develop a fully automated segmentation and morphometric parameter estimation system for assessing corneal endothelial cells from in vivo confocal microscopy images.
Artificial intelligence (neural network) study.
First, a fully automated deep learning system for assessing corneal endothelial cells was developed using the development set (from 99 subjects). Second, 184 images (from 97 subjects) were used to construct the testing set to evaluate the clinical validity and usefulness of the automated segmentation and morphometric system. Third, the automatically calculated endothelial cell density (ECD) values, Topcon's cell density, and manually calculated ECD were compared.
After slit lamp examination, 88 healthy subjects, 2 Fuchs endothelial dystrophy patients, and 7 corneal endotheliitis patients were identified among the 97 subjects in the testing set. The automatedly estimated morphometric parameters for the testing set were an average number of 234 cells, an ECD of 2592 cells/mm, a coefficient of variation in the cell area of 32.14%, and a percentage of hexagonal cells of 54.16%. Pearson's correlation coefficient between the automated ECD and Topcon's cell density and between the manually calculated ECD and Topcon's cell density was 0.932 (P < .01) and 0.818 (P < .01), respectively. The Bland-Altman plot of Topcon's cell density and the automated ECD yielded 95% limits of agreement between 271.94 and -572.46 (concordance correlation coefficient = 0.9).
A fully automated method for segmenting corneal endothelial cells and estimating morphometric parameters using in vivo confocal microscopy images is more efficient and accurate for assessing the normal corneal endothelium.
开发一种用于评估活体共聚焦显微镜图像中角膜内皮细胞的全自动分割和形态参数估计系统。
人工智能（神经网络）研究。
首先，使用开发集（来自 99 位受试者）开发了一种用于评估角膜内皮细胞的全自动深度学习系统。其次，使用 184 张图像（来自 97 位受试者）构建测试集，以评估自动分割和形态系统的临床有效性和实用性。第三，比较了自动计算的内皮细胞密度（ECD）值、Topcon 的细胞密度和手动计算的 ECD 值。
在裂隙灯检查后，在测试集中的 97 位受试者中，有 88 位健康受试者、2 位 Fuchs 内皮营养不良患者和 7 位角膜内皮炎患者。测试集的自动估计形态参数为平均 234 个细胞、ECD 为 2592 个细胞/mm、细胞面积变异系数为 32.14%和六边形细胞百分比为 54.16%。自动 ECD 与 Topcon 细胞密度之间以及手动计算的 ECD 与 Topcon 细胞密度之间的 Pearson 相关系数分别为 0.932（P <.01）和 0.818（P <.01）。Topcon 细胞密度和自动 ECD 的 Bland-Altman 图得出 95%一致性界限为 271.94 至-572.46（一致性相关系数为 0.9）。
使用活体共聚焦显微镜图像自动分割角膜内皮细胞和估计形态参数的方法对于评估正常角膜内皮更加高效和准确。
