Zhao Chaoyang, Li Huilin, Yuan Ziyou, Yang Zihan, Wang Tiantian, Wang Yan, Tong Qian, Hao Shaofeng
Department of Ophthalmology, Heji Hospital Affiliated with Changzhi Medical College, Changzhi, China.
Graduate Office, Changzhi Medical College, Changzhi, Shanxi, China.
PLoS One. 2025 Jun 17;20(6):e0324352. doi: 10.1371/journal.pone.0324352. eCollection 2025.
This study aims to utilize artificial intelligence technology to conduct an in-depth analysis of fundus data from myopic children and adolescents, thoroughly exploring the correlation between retinal vascular parameters and axial length (AL), and ultimately revealing the changing patterns of retinal vascular characteristics in children with different refractive errors. The findings aim to provide a scientific basis for the prevention, early screening, and formulation of personalized treatment strategies for myopia.
The study selected 124 students from Jiandong Primary School in Changzhi City who underwent myopia prevention and control screening. Their axial length data were recorded, and fundus photographs were taken using the Topcon TNF506 non-mydriatic fundus camera. Subsequently, these fundus images were meticulously analyzed using the EVision AI fundus image analysis system, which is a commercial software that employs pre-trained algorithms to automatically extract retinal vascular parameters.Pearson and Spearman correlation coefficients were used to analyze the correlation between retinal vascular parameters and axial length, and multiple linear regression analysis was further conducted to explore their intrinsic associations.
The study found that in the low myopia group, axial length was significantly negatively correlated with various retinal vascular parameters, including the average diameters of arteries and veins, average vascular tortuosity, atrophy arc area, and leopard spot density. In the moderate to high myopia group, axial length also showed significant negative correlations with the average diameter of arteries, some average venous tortuosity, and average vascular diameter. However, fractal dimension of vessels and average branch angle did not show significant changes across all myopia groups.
This study clearly demonstrates a significant correlation between axial length and retinal vascular parameters, with notable differences in this correlation among children with different refractive errors. These findings not only provide a new perspective for understanding the pathological mechanisms of myopia but also offer important scientific evidence for the development of more precise and personalized myopia prevention and control strategies in the future. They have potential guiding significance for clinical practice and policy formulation.
本研究旨在利用人工智能技术对近视儿童及青少年的眼底数据进行深入分析，全面探究视网膜血管参数与眼轴长度（AL）之间的相关性，并最终揭示不同屈光不正儿童视网膜血管特征的变化规律。研究结果旨在为近视的预防、早期筛查及个性化治疗策略的制定提供科学依据。
本研究选取了长治市健东小学124名接受近视防控筛查的学生。记录他们的眼轴长度数据，并使用Topcon TNF506免散瞳眼底相机拍摄眼底照片。随后，使用EVision AI眼底图像分析系统对这些眼底图像进行细致分析，该系统是一款商业软件，采用预训练算法自动提取视网膜血管参数。采用Pearson和Spearman相关系数分析视网膜血管参数与眼轴长度之间的相关性，并进一步进行多元线性回归分析以探究它们的内在关联。
研究发现，在低度近视组中，眼轴长度与多种视网膜血管参数呈显著负相关，包括动脉和静脉的平均直径、平均血管迂曲度、萎缩弧面积和豹纹状斑密度。在中度至高度近视组中，眼轴长度与动脉平均直径、部分平均静脉迂曲度和平均血管直径也呈显著负相关。然而，血管分形维数和平均分支角度在所有近视组中均未显示出显著变化。
本研究明确表明眼轴长度与视网膜血管参数之间存在显著相关性，且不同屈光不正儿童在这种相关性上存在显著差异。这些发现不仅为理解近视的病理机制提供了新的视角，也为未来制定更精确、个性化的近视防控策略提供了重要的科学依据。它们对临床实践和政策制定具有潜在的指导意义。
