Application of Artificial Intelligence to Quantitative Assessment of Fundus Tessellated Density in Young Adults with Different Refractions

Abstract

Introduction: The aim of this study was to quantitatively assess fundus tessellated density (FTD) and associated factors by artificial intelligence (AI) in young adults. Methods: A total of 1,084 undergraduates (age, 17–23 years old) were enrolled in November 2021. The students were divided into three groups according to axial length (AL): group 1 (AL <24.0 mm, n = 155), group 2 (24 mm ≤ AL <26 mm, n = 578), and group 3 (AL ≥26 mm, n = 269). FTD was calculated by extracting the fundus tessellations as the regions of interest (circle 1, diameter of 3.0 mm; circle 2, diameter of 6.0 mm) and then calculating the average exposed choroid area per unit area of fundus. Results: Among 1,084 students, 1,002 (92.5%) students' FTDs were extracted. The mean FTD was 0.06 ± 0.06 (range, 0–0.40). In multivariate analysis, FTD was significantly associated with male sex, longer AL, thinner subfoveal choroid thickness (SFCT), increased choriocapillaris vessel density (VD), and decreased deeper choroidal VD (all p < 0.05). In circle 1 (diameter of 3.0 mm) and circle 2 (diameter of 6.0 mm), analysis of variance showed that the FTD of the nasal region (p < 0.05) was significantly larger than that of the superior, inferior, and temporal regions. Conclusion: AI-based imaging processing could improve the accuracy of fundus tessellation diagnosis. FTD was significantly associated with a longer AL, thinner SFCT, increased choriocapillaris VD, and decreased deeper choroidal VD.