Jing Dalan, Liu Yushi, Chou Yilin, Jiang Xiaodan, Ren Xiaotong, Yang Luling, Su Jie, Li Xuemin
Department of Ophthalmology, Peking University Third Hospital, Beijing, People's Republic of China; Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, People's Republic of China.
Department of Ophthalmology, Peking University Third Hospital, Beijing, People's Republic of China; Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, People's Republic of China.
Exp Eye Res. 2022 Feb;215:108851. doi: 10.1016/j.exer.2021.108851. Epub 2021 Dec 10.
We aimed to investigate the change patterns in corneal sub-basal nerve morphology and corneal intrinsic aberrations in dry eye disease (DED). Our study included 229 eyes of 155 patients with DED and 40 eyes of 20 healthy control. We used the Oculus keratograph and the ocular surface disease index questionnaire to assess their signs and symptoms. In vivo confocal microscopy was used to observe the corneal sub-basal nerves, corneal endothelial cells, and Langerhans cells (LCs). An artificial intelligence (AI) technique run by the deep learning model generated the sub-basal nerve fibre parameters. Furthermore, we used the Pentacam HR system to measure the corneal intrinsic aberrations and corneal surface regularity indices. DED patients more frequently had increased anterior and total corneal aberrations than controls (P < 0.05). In addition, DED had decreased average density and maximum length of corneal nerve. (Both P < 0.01) The LC number was significantly correlated with maximum length (CC = -0.19, P = 0.01) of the sub-basal nerve fibre. Furthermore, the corneal nerve average density was negatively correlated with IHD, and anterior, posterior, and total corneal aberrations (All P < 0.05) especially the higher-order aberrations. Significant correlations were seen between corneal nerve morphology changes, analysed by AI and corneal intrinsic aberrations, particularly higher-order aberrations.
我们旨在研究干眼病（DED）患者角膜基底膜下神经形态和角膜固有像差的变化模式。我们的研究纳入了155例DED患者的229只眼以及20名健康对照者的40只眼。我们使用欧科路角膜地形图仪和眼表疾病指数问卷来评估其体征和症状。采用共焦显微镜在体观察角膜基底膜下神经、角膜内皮细胞和朗格汉斯细胞（LCs）。由深度学习模型运行的人工智能（AI）技术生成基底膜下神经纤维参数。此外，我们使用Pentacam HR系统测量角膜固有像差和角膜表面规则性指数。DED患者比对照组更频繁地出现角膜前表面和总像差增加（P < 0.05）。此外，DED患者角膜神经平均密度和最大长度降低（均P < 0.01）LC数量与基底膜下神经纤维的最大长度显著相关（CC = -0.19，P = 0.01）。此外，角膜神经平均密度与眼内高压、角膜前表面、后表面和总像差呈负相关（均P < 0.05），尤其是高阶像差。通过AI分析的角膜神经形态变化与角膜固有像差，特别是高阶像差之间存在显著相关性。
