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

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.