Dissecting the Profile of Corneal Thickness With Keratoconus Progression Based on Anterior Segment Optical Coherence Tomography

Yanling Dong; Dongfang Li; Zhen Guo; Yang Liu; Ping Lin; Bin Lv; Chuanfeng Lv; Guotong Xie; Lixin Xie

doi:10.3389/fnins.2021.804273

Dissecting the Profile of Corneal Thickness With Keratoconus Progression Based on Anterior Segment Optical Coherence Tomography

Front Neurosci. 2022 Jan 31:15:804273. doi: 10.3389/fnins.2021.804273. eCollection 2021.

Authors

Yanling Dong^{1

2}, Dongfang Li^{1

2}, Zhen Guo^{1

2}, Yang Liu³, Ping Lin^{1

2}, Bin Lv³, Chuanfeng Lv³, Guotong Xie^{3

4

5}, Lixin Xie^{1

2}

Affiliations

¹ Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China.
² State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Eye Institute of Shandong First Medical University, Qingdao, China.
³ Ping An Technology (Shenzhen) Co. Ltd., Shenzhen, China.
⁴ Ping An Health Cloud Co. Ltd., Shenzhen, China.
⁵ Ping An International Smart City Technology Co. Ltd., Shenzhen, China.

Abstract

Purpose: To characterize the corneal and epithelial thickness at different stages of keratoconus (KC), using a deep learning based corneal segmentation algorithm for anterior segment optical coherence tomography (AS-OCT).

Methods: An AS-OCT dataset was constructed in this study with 1,430 images from 715 eyes, which included 118 normal eyes, 134 mild KC, 239 moderate KC, 153 severe KC, and 71 scarring KC. A deep learning based corneal segmentation algorithm was applied to isolate the epithelial and corneal tissues from the background. Based on the segmentation results, the thickness of epithelial and corneal tissues was automatically measured in the center 6 mm area. One-way ANOVA and linear regression were performed in 20 equally divided zones to explore the trend of the thickness changes at different locations with the KC progression. The 95% confidence intervals (CI) of epithelial thickness and corneal thickness in a specific zone were calculated to reveal the difference of thickness distribution among different groups.

Results: Our data showed that the deep learning based corneal segmentation algorithm can achieve accurate tissue segmentation and the error range of measured thickness was less than 4 μm between our method and the results from clinical experts, which is approximately one image pixel. Statistical analyses revealed significant corneal thickness differences in all the divided zones (P < 0.05). The entire corneal thickness grew gradually thinner with the progression of the KC, and their trends were more pronounced around the pupil center with a slight shift toward the temporal and inferior side. Especially the epithelial thicknesses were thinner gradually from a normal eye to severe KC. Due to the formation of the corneal scarring, epithelial thickness had irregular fluctuations in the scarring KC.

Conclusion: Our study demonstrates that our deep learning method based on AS-OCT images could accurately delineate the corneal tissues and further successfully characterize the epithelial and corneal thickness changes at different stages of the KC progression.

Keywords: anterior segment optical coherence tomography; corneal thickness; deep learning; keratoconus; segmentation.