The impact of dry eye disease on corneal nerve parameters: A systematic review and meta‐analysis

Abstract

PurposeDry eye disease (DED) is a growing global health problem with a significant impact on the quality of life of patients. While neurosensory abnormalities have been recognised as a contributor to DED pathophysiology, the potential role of in vivo corneal confocal microscopy in detecting nerve loss or damage remains unclear. This systematic review with meta‐analysis (PROSPERO registered CRD42022381861) investigated whether DED has an impact on sub‐basal corneal nerve parameters.MethodsPubMed, Embase and Web of Science Core Collection databases were searched from inception to 9 December 2022. Studies using laser scanning confocal microscopy to compare corneal nerve parameters of DED with healthy eyes were included. Study selection process and data extraction were performed by two independent members of the review team.ResultsTwenty‐two studies with 916 participants with DED and 491 healthy controls were included, with 21 of these studies included in subsequent meta‐analyses. There was a decrease in total corneal nerve length (−3.85 mm/mm2; 95% CI −5.16, −2.55), corneal main nerve trunk density (−4.81 number/mm2; 95% CI −7.94, −1.68) and corneal nerve branch density (−15.52 number/mm2; 95% CI −27.20, −3.84) in DED eyes compared with healthy eyes, with subgroup analysis demonstrating that these differences were more evident in studies using NeuronJ software, a semi‐automated procedure. While this review found evidence of loss of corneal nerve parameters in eyes with DED compared with healthy controls, particularly with the use of a semi‐automated image analysis method, it is evident that there is substantial heterogeneity between studies in terms of corneal nerve imaging methodology.ConclusionsStandardisation is required in terms of terminology and analysis, with more research needed to potentially improve the clinical applicability and practicality of corneal nerve imaging. Further investigation is also required to confirm the diagnostic accuracy of this imaging modality and its potential for monitoring DED treatment efficacy.

Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review

“…In their next study [90], the same algorithm measured sub-basal nerve parameters to investigate the association between oval cells, Langerhans cells (LCs), and dry eye disease. In opposition to the studies [91,92] showing a decrease in the nerve length, it was observed that-with the presence of the LCs and bright, oval cells-there was a greater corneal peripheral nerve maximum length and average density. This suggests that the changes in corneal nerve density and nerve number are related to the level of advancement of the dry eye disease.…”

“…In their next study [90], the same algorithm measured subbasal nerve parameters to investigate the association between oval cells, Langerhans cells (LCs), and dry eye disease. In opposition to the studies [91,92] showing a decrease in the Shanshan Wei et al [88] designed a deep learning model called CNS-Net, which was designed to analyze sub-basal nerve morphologies. It allowed for the possibility of obtaining the average density and the maximum length of the nerve fiber with a high accuracy that produced an AUC of 96%.…”