ReviewFrontiers in medicine2026
Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- 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
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Fundus tessellation (FT) is increasingly recognized as an early structural manifestation of retinal and choroidal remodeling in myopia, reflecting initial changes associated with axial elongation. With advances in artificial intelligence (AI), particularly deep learning-based image analysis, quantitative assessment of FT has emerged as a promising approach for objective and scalable evaluation in myopia research. Methods: This review provides an integrative overview of recent studies applying AI-assisted and quantitative image-analysis approaches to fundus tessellation assessment. Relevant literature was identified through a structured search of major biomedical databases, focusing on methodologies for FT and fundus tessellation density (FTD) quantification and their reported associations with clinical ocular parameters in myopia. Results: Across multiple cohorts, AI-derived FTD consistently showed associations with axial length elongation, choroidal thinning, and increasing myopia severity. Common analytical frameworks involved region-of-interest definition, image normalization, and supervised deep learning-based segmentation. Several studies further reported spatial heterogeneity in FTD distribution and its relationship with peripapillary and macular alterations, supporting the potential role of FT-related metrics as quantitative imaging biomarkers for myopic structural change. Conclusion: AI-driven quantification of fundus tessellation represents a methodological advancement in myopia research by enhancing objectivity and scalability in retinal image analysis. These approaches may facilitate early risk stratification and hold promise for future longitudinal assessment of myopia.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.