ArticleTranslational vision science & technology2023
Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.
Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- Study on fundus characteristics of preschool children based on artificial intelligence quantitative technology.BMC ophthalmology · 2026Observational
- Artificial intelligence assisted quantitative analysis of fundus structural and vascular alterations reveals fundus tessellation as a robust biomarker of myopia severity.Frontiers in cell and developmental biology · 2026Article
- Artificial intelligence-based quantitative analysis of interocular retinal vascular differences in school-age children with mild to moderate anisometropia.Frontiers in medicine · 2026Article
- Artificial intelligence-quantified fundus tessellated density and its association with myopia severity in Chinese children: a cross-sectional study.Frontiers in cell and developmental biology · 2026Article
- Defining the critical axial length threshold for transition from physiological growth to pathological fundus structural remodeling in adolescent myopia.Frontiers in cell and developmental biology · 2026Article
- Association between fundus tessellated density and subfoveal choroidal thickness in children with different refractive statuses.Frontiers in medicine · 2026Article
- Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.Frontiers in medicine · 2026Review
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Fundus vascular arcades angle reflects choroidal thickness in highly myopic children and adolescents.Eye (London, England) · 2025Article
- The Associations Between Myopia and Fundus Tessellation in School Children: A Comparative Analysis of Macular and Peripapillary Regions Using Deep Learning.Translational vision science & technology · 2025Article
- Artificial intelligence-assisted quantification of fundus tessellation in early-onset high myopia.Frontiers in medicine · 2025Article
- Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.BMJ open ophthalmology · 2024Article
- Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning.Translational vision science & technology · 2024Article
- Regional choroidal thickness estimation from color fundus images based on convolutional neural networks.Heliyon · 2024Article
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. Methods: Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. Results: The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). Conclusions: FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. Translational Relevance: Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control.
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