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Updated: Aug 8, 2025
Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
Published on: March 11, 2016
Application of Artificial Intelligence to Quantitative Assessment of Fundus Tessellated Density in Young Adults with
Runkuan Li1, Xiaoxiao Guo2, Xiuyan Zhang3,4
1Shandong University of Traditional Chinese Medicine, Jinan, China, 1326915678@qq.com.
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.
