Corneal pachymetry by AS-OCT after Descemet\u2019s membrane endothelial keratoplasty

Friso G Heslinga,Javier Cabrerizo,Mark Alberti,Myrthe A Van Den Berg,Josien P W Pluim,Mitko Veta,Ruben T Lucassen,Luuk Van Der Hoek

doi:10.1038/s41598-021-93186-9

Abstract

Corneal thickness (pachymetry) maps can be used to monitor restoration of corneal endothelial function, for example after Descemet’s membrane endothelial keratoplasty (DMEK). Automated delineation of the corneal interfaces in anterior segment optical coherence tomography (AS-OCT) can be challenging for corneas that are irregularly shaped due to pathology, or as a consequence of surgery, leading to incorrect thickness measurements. In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three different deep learning strategies were developed based on 960 B-scans from 50 patients. On an independent test set of 320 B-scans, corneal thickness could be measured with an error of 13.98 to 15.50 μm for the central 9 mm range, which is less than 3% of the average corneal thickness. The accurate thickness measurements were used to construct detailed pachymetry maps. Moreover, follow-up scans could be registered based on anatomical landmarks to obtain differential pachymetry maps. These maps may enable a more comprehensive understanding of the restoration of the endothelial function after DMEK, where thickness often varies throughout different regions of the cornea, and subsequently contribute to a standardized postoperative regime.

Highlights

Corneal thickness is a key biomarker for corneal disorders, including Fuchs’ endothelial dystrophy1,2, keratoconus[3,4], and keratitis[5,6]
We hypothesized that a similar approach could be used for corneal pachymetry for cases with irregular inner corneal curvature and/or structures that look similar to the corneal boundaries, both of which can lead to delineation failures by standard anterior segment optical coherence tomography (AS-OCT) software
In this study we focus on OCT scans acquired after Descemet’s Membrane Endothelial Keratoplasty (DMEK)[16]

Summary

Introduction

Corneal thickness is a key biomarker for corneal disorders, including Fuchs’ endothelial dystrophy1,2, keratoconus[3,4], and keratitis[5,6]. Deep learning is a subset of machine learning techniques with models that contain many (typically millions of) trainable weights. These weights are iteratively updated with respect to some loss function which compares model predictions to ground truth labels. After placement of the graft, a gas bubble is injected into the anterior chamber to support graft attachment to the host cornea Both the procedural gas bubble and donor graft can mimic the appearance of the corneal interface and result in incorrect delineation. We validate our thickness measurements for the central 9 mm diameter (Fig. 1), whereas previous work only did so for 3.1 mm[15] This is essential to assess corneal regeneration after DMEK surgery which uses a graft of ~ 8.5 mm. 3 mm 6 mm 9 mm differential pachymetry maps that locates the center of the cornea in subsequent images and visualizes thickness differences over time

Methods

Results

Conclusion