ArticleBMC ophthalmology2024
A preliminary study of artificial intelligence to recognize tessellated fundus in visual function screening of 7-14 year old students.
Article in BMC ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.Frontiers in medicine · 2026Review
- Longitudinal changes in macular curvature and axial elongation in school children.International journal of retina and vitreous · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundTo evaluate the accuracy of artificial intelligence (AI)-based technology in recognizing tessellated fundus in students aged 7-14 years.
methodsA retrospective study was conducted to collect consecutive fundus photographs for visual function screening of students aged 7-14 years old in Haikou City from June 2018 to May 2019, and 1907 cases were included in the study. Among them, 949 cases were male and 958cases were female. The results were manually analyzed by two attending ophthalmologists to ensure the accuracy of the results. In case of discrepancies between the results analyzed by the two methods, the manual results were used as the standard. To assess the sensitivity and specificity of AI in recognizing tessellated fundus, a Kappa consistency test was performed comparing the results of manual recognition with those of AI recognition.
resultsAmong 1907 cases, 1782 cases, or 93.4%, were completely consistent with the recognition results of manual and AI; 125 cases, or 6.6%, were analyzed with differences. The diagnostic rates of manual and AI for tessellated fundus were 26.1% and 26.4%, respectively. The sensitivity, specificity and area of the ROC curve (AUC) of AI for recognizing tessellated fundus in students aged 7-14 years were 88.0%, 95.4% and 0.917, respectively. The results of test showed that that the manual and AI identification results were highly consistent (κ = 0.831, P = 0.000).
conclusionAI analysis has high specificity and sensitivity for tessellated fundus identification in students aged 7-14 years, and it is feasible to apply artificial intelligence to visual function screening in students aged 7-14 years.
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