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利用人工智能对屈光不正学龄儿童视网膜血管参数变化进行定量分析。

Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using artificial intelligence.

作者信息

Liu Linlin, Zhong Lijie, Zeng Linggeng, Liu Fang, Yu Xinghui, Xie Lianfeng, Tan Shuxiang, Zhang Shuang, Jiang Yi-Ping

机构信息

The Department of Ophthalmology of the First Affiliated Hospital, Gannan Medical University, Ganzhou, Jiangxi, China.

Postgraduates at the First Clinical Medicine of Gannan Medical University, Ganzhou, Jiangxi, China.

出版信息

Front Med (Lausanne). 2024 Dec 31;11:1528772. doi: 10.3389/fmed.2024.1528772. eCollection 2024.

Abstract

AIM

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 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 ( < 0.05). Linear correlation analysis showed a strong negative correlation between the spherical equivalent refraction and the ocular axis ( = -0.874,  < 0001), a moderate positive correlation between the spherical equivalent refraction and the vascular density ( = 0.527,  < 0001), and a moderate negative correlation between the ocular axis and the vascular density ( = -0.452,  < 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.

摘要

目的

应用眼底摄影结合人工智能(AI)技术,定量分析学龄期屈光不正儿童的等效球镜度(SER)与视网膜血管变化之间的关系,并探讨这些儿童视网膜血管的结构变化。

方法

我们进行了一项回顾性病例对照研究,收集了2021年10月至2022年5月期间在我院门诊就诊的113例6-12岁学龄儿童的数据,共涉及226只眼。根据屈光等效球镜度,将参与者分为四组:低度近视组66只眼,中度近视组60只眼,高度近视组50只眼,对照组50只眼。所有参与者均接受了一系列检查,包括裸眼视力和最佳矫正视力、散瞳等效球镜度、眼压测量、眼轴测量(AL)和彩色眼底摄影。利用眼底摄影,我们采用AI技术定量分析了视网膜血管动静脉比(AVR)、平均曲率和血管密度的变化。数据采用χ检验和单因素方差分析。

结果

低度近视组、中度近视组、高度近视组和对照组的AVR分别为0.80±0.05、0.80±0.04、0.76±0.04和0.79±0.04,血管密度分别为0.1024±0.0076、0.1024±0.0074、0.0880±0.0126和0.1037±0.0143。与其他三组相比,高度近视组的AVR和血管密度差异具有统计学意义(P<0.05)。线性相关分析显示,等效球镜度与眼轴呈强负相关(r=-0.874,P<0.001),等效球镜度与血管密度呈中度正相关(r=0.527,P<0.001),眼轴与血管密度呈中度负相关(r=-0.452,P<0.001)。

结论

高度近视学龄儿童视网膜血管的AVR降低,血管密度降低。AVR和血管密度可能是近视进展的早期预测指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8370/11729343/7ba4951db91a/fmed-11-1528772-g001.jpg

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