Quantitative Assessment of Fundus Tessellated Density and Associated Factors in Fundus Images Using Artificial Intelligence.

PURPOSE

This study aimed to quantitative assess the fundus tessellated density (FTD) and associated factors on the basis of fundus photographs using artificial intelligence.

METHODS

A detailed examination of 3468 individuals was performed. The proposed method for FTD measurements consists of image preprocessing, sample labeling, deep learning segmentation model, and FTD calculation. Fundus tessellation was extracted as region of interest and then the FTD could be obtained by calculating the average exposed choroid area of per unit area of fundus. Besides, univariate and multivariate linear regression analysis have been conducted for the statistical analysis.

RESULTS

The mean FTD was 0.14 ± 0.08 (median, 0.13; range, 0-0.39). In multivariate analysis, FTD was significantly (P < 0.001) associated with thinner subfoveal choroidal thickness, longer axial length, larger parapapillary atrophy, older age, male sex and lower body mass index. Correlation analysis suggested that the FTD increased by 33.1% (r = 0.33, P < .001) for each decade of life. Besides, correlation analysis indicated the negative correlation between FTD and spherical equivalent (SE) in the myopia participants (r = -0.25, P < 0.001), and no correlations between FTD and SE in the hypermetropia and emmetropic participants.

CONCLUSIONS

It is feasible and efficient to extract FTD information from fundus images by artificial intelligence-based imaging processing. FTD can be widely used in population screening as a new quantitative biomarker for the thickness of the subfoveal choroid. The association between FTD with pathological myopia and lower visual acuity warrants further investigation.

TRANSLATIONAL RELEVANCE

Artificial intelligence can extract valuable clinical biomarkers from fundus images and assist in population screening.