Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus’ inferior whorl in patients with neuropathic corneal pain and dry eye disease

Abstract

Purpose

To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal microscopy (IVCM) images.

Methods

Subbasal nerve plexus (SNP) images of the inferior whorl of patients with DED (n = 49, 77 eyes), NCP (n = 14, 24 eyes), and controls (n = 41, 59 eyes) were taken with IVCM and further analyzed using an open-source artificial intelligence (AI)-based algorithm previously developed by our group. This algorithm automatically segments nerves, immune cells, and neuromas in the SNP. The following parameters were compared between groups: nerve area density, average nerve thickness, average nerve segment tortuosity, junction point density, neuroma density, and immune cell density.

Results

160 eyes of 104 patients (63 % females), aged 56.8 ± 15.4 years, were included. The mean nerve area density was significantly lower in the DED (P = 0.012) and NCP (P < 0.001) groups compared to the control group. The junction point density was lower in the NCP group compared with control (P = 0.001) and DED (P = 0.004) groups. The immune cell density was higher in the DED group compared with controls (P < 0.001).

Conclusions

Deep-learning-based analysis of IVCM images of the corneal SNP inferior whorl distinguished a decreased mean nerve area density in patients with DED and NCP compared with controls and an increased immune cell density in patients with oGVHD- and SS-associated DED. These findings suggest that the inferior whorl could be used as landmark to distinguish between patients with DED and NCP.