Automated measurement and correlation analysis of fundus tessellation and optic disc characteristics in myopia.

This study aims to quantify fundus tessellated (FT) density and optic disc (OD) morphology using deep learning (DL) techniques and to investigate the correlations between these fundus characteristics and refractive function in young patients with myopia. We constructed two DL-based segmentation models to delineate the FT, OD, peripapillary atrophy (PPA), and macula at a pixel-level resolution. The study sought to identify differences in fundus characteristics between eyes categorized as having high myopia versus mild or moderate myopia. Furthermore, the correlation between fundus measurements and various ocular parameters was statistically analyzed. Correlation analysis indicated that the spherical equivalent and axial length were significantly associated with all fundus measurements (p < 0.001). Additionally, corneal curvature (K1, K2), lens thickness, and foveal thickness exhibited significant correlations with some of the fundus measurements at a 0.01 significance level. Using DL algorithms, it is feasible to automatically quantify FT and OD characteristics in young myopic patients. The study findings suggest that both FT and OD characteristics are highly correlated with the severity of myopia, particularly as it progresses from mild or moderate to high levels. Moreover, a significant relationship exists between most of these fundus characteristics and a spectrum of refractive function parameters.