ArticleTranslational vision science & technology2024
A Novel Artificial Intelligence-Based Classification of Highly Myopic Eyes Based on Visual Function and Fundus Features.
Article in Translational vision science & technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Low-Dose Atropine and Other Interventions for Pediatric Myopia: A Systematic Review and Bayesian Network Meta-analysis of Efficacy and Post-treatment Outcomes.Ophthalmology and therapy · 2026Review
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.Translational vision science & technology · 2025Article
- Using Hierarchical Bayesian Modeling to Enhance Statistical Inference on Contrast Sensitivity.Translational vision science & technology · 2024Article
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Authors and funding
11 authors.
Funding
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Abstract
Purpose: To develop a novel classification of highly myopic eyes using artificial intelligence (AI) and investigate its relationship with contrast sensitivity function (CSF) and fundus features. Methods: We enrolled 616 highly myopic eyes of 616 patients. CSF was measured using the quantitative CSF method. Myopic macular degeneration (MMD) was graded according to the International META-PM Classification. Thickness of the macula and peripapillary retinal nerve fiber layer (p-RNFL) were assessed by fundus photography and optical coherence tomography, respectively. Classification was performed by combining CSF and fundus features with principal component analysis and k-means clustering. Results: With 83.35% total variance explained, highly myopic eyes were classified into four AI categories. The percentages of AI categories 1 to 4 were 14.9%, 37.5%, 36.2%, and 11.4%, respectively. Contrast acuity of the eyes in AI category 1 was the highest, which decreased by half in AI category 2. For AI categories 2 to 4, every increase in category led to a decrease of 0.23 logarithm of the minimum angle of resolution in contrast acuity. Compared with those in AI category 1, eyes in AI category 2 presented a higher percentage of MMD2 and thinner temporal p-RNFL. Eyes in AI categories 3 and 4 presented significantly higher percentage of MMD ≥ 3, thinner nasal macular thickness and p-RNFL (P < 0.05). Multivariate regression showed AI category 4 had higher MMD grades and thinner macular compared with AI category 3. Conclusions: We proposed an AI-based classification of highly myopic eyes with clear relevance to visual function and fundus features. Translational Relevance: This classification helps to discover the early hidden visual deficits of highly myopic patients, becoming a useful tool to evaluate the disease comprehensively.
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