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ORIGINAL RESEARCH article

Front. Med.
Sec. Ophthalmology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1528772
This article is part of the Research Topic Imaging in the Diagnosis and Treatment of Eye Diseases View all 10 articles

Quantitative analysis of retinal vascular parameters changes in schoolage children with refractive error using artificial intelligence

Provisionally accepted
Linlin Liu Linlin Liu *LiJie Zhong LiJie Zhong Linggeng Zeng Linggeng Zeng Fang Liu Fang Liu Xinghui Yu Xinghui Yu Lianfeng Xie Lianfeng Xie Shuxiang Tan Shuxiang Tan Shuang Zhang Shuang Zhang Yi-Ping Jiang Yi-Ping Jiang
  • Gannan Medical University, Ganzhou, China

The final, formatted version of the article will be published soon.

    To quantitatively analyze the relationship between spherical equivalent refraction (SER) and retinal vascular changes in school-age children with refractive error by applying fundus photography combined with artificial intelligence (AI) technology and explore the structural changes in retinal vasculature in these children. Methods: We conducted a retrospective case-control study, collecting data on 113 cases involving 226 eyes of schoolchildren aged 6-12 years who attended outpatient clinics in our hospital between October 2021 and May 2022. Based on the refractive spherical equivalent refraction , we categorized the participants into four groups: 66 eyes in the low myopia group, 60 eyes in the intermediate myopia group, 50 eyes in the high myopia group, and 50 eyes in the control group. All participants underwent a series of examinations, including naked-eye and best- corrected visual acuity, cycloplegic spherical equivalent refraction, intraocular pressure measurement, ocular axial measurement(AL), and color fundus photography. Using fundus photography, we quantitatively analyzed changes in the retinal vascular arteriovenous ratio (AVR), average curvature, and vascular density with AI technology. Data were analyzed using the χ 2 test and one-way analysis of variance. Results: The AVR in the low myopia group, moderate myopia group, high myopia group, and control group were 0.80±0.05, 0.80±0.04, 0.76±0.04, and 0.79±0.04, respectively, and the vessel densities were 0.1024±0.0076, 0.1024±0.0074, 0.0880±0.0126, and 0.1037±0.0143, respectively The difference between the AVR and vascular density in the high myopia group was statistically significant compared to the other three groups (P<0.05). Linear correlation analysis showed a strong negative correlation between the spherical equivalent refraction and the ocular axis (r= -0.874, P<0001), a moderate positive correlation between the spherical equivalent refractionand the vascular density (r=0.527, P<0001), and a moderate negative correlation between the ocular axis and the vascular density (r= -0.452, P<0001). Conclusion: Schoolchildren with high myopia showed a decreased AVR and decreased vascular density in the retinal vasculature. The AVR and vascular density may be early predictors of myopia progression.

    Keywords: artificial intelligence, quantitative analysis, Ametropia, Retinal vascular changes, School-age children

    Received: 15 Nov 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Liu, Zhong, Zeng, Liu, Yu, Xie, Tan, Zhang and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Linlin Liu, Gannan Medical University, Ganzhou, China

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