Artificial intelligence–assisted quantification of fundus tessellation in early-onset high myopia
Yi-Ming Guo, Kexin Wu, Juan Huang, Bo Ma, Lu Ye

TL;DR
AI helps measure retinal patterns in children with high myopia, linking these patterns to eye length and suggesting their use for early diagnosis.
Contribution
AI-assisted quantification of fundus tessellation density as a novel biomarker for early-onset high myopia in children.
Findings
FTD decreases from the center to the periphery of the retina (p < 0.001).
FTD in nasal and inferior sectors at 6 mm is significantly higher.
FTD correlates with axial length (r = 0.46) and predicts it in regression models.
Abstract
To quantitatively evaluate fundus tessellation density (FTD) in children with early-onset high myopia (eo-HM) using artificial intelligence (AI)–assisted image analysis and to explore its association with axial length (AL). This cross-sectional study included children aged ≤6 years with eo-HM, defined as spherical equivalent (SE) ≤ −6.00 D and/or AL > 26.00 mm. Cycloplegic refraction, AL measurement, and ultra-widefield fundus photography were performed. A deep learning–based algorithm quantified FTD across concentric macular zones (1 mm, 3 mm, 6 mm) and anatomical sectors. Correlation and multivariate regression analyses were used to assess associations between FTD and AL. A total of 47 eyes from 31 children were analyzed. Mean SE was −9.35 ± 3.74 D and AL was 25.70 ± 1.50 mm. FTD declined from center to periphery (p < 0.001), with greater values in nasal and inferior sectors at 6…
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Taxonomy
TopicsOphthalmology and Visual Impairment Studies · Glaucoma and retinal disorders · Retinopathy of Prematurity Studies
