深度学习评估小学生眼底网格状密度。
Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.
机构信息
Department of Ophthalmology, The First Affiliated Hospital With Nanjing Medical University, Nanjing, Jiangsu, China.
EVision Technology (Beijing) Co. LTD, Beijing, China.
出版信息
Transl Vis Sci Technol. 2023 Jun 1;12(6):11. doi: 10.1167/tvst.12.6.11.
PURPOSE
To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning.
METHODS
Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD.
RESULTS
The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05).
CONCLUSIONS
FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern.
TRANSLATIONAL RELEVANCE
Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control.
目的
利用人工智能技术中的深度学习，探讨眼底镶嵌密度（FTD）的相关性，并比较不同眼底镶嵌（FT）分布模式的特征。
方法
在一项基于人群的横断面研究中，对 577 名 7 岁儿童进行了全面的眼科检查，包括生物测量、屈光、光相干断层扫描血管造影和 45°非散瞳眼底照相。FTD 定义为单位眼底面积暴露脉络膜的平均面积，通过人工智能技术获得。根据 FTD 将 FT 分布分为黄斑型和视盘旁型。
结果
全眼底平均 FTD 为 0.024±0.026。多变量回归分析显示，较大的 FTD 与较薄的黄斑中心凹下脉络膜厚度、较大的视盘旁萎缩、较大的视盘内血管密度、较大的视盘垂直直径、较薄的视网膜神经纤维层以及视盘中心到黄斑中心凹的距离较长（均 P<0.05）显著相关。视盘旁分布组视盘旁萎缩较大（0.052±0.119 比 0.031±0.072），FTD 较大（0.029±0.028 比 0.015±0.018），黄斑中心凹下脉络膜厚度较薄（297.66±60.61 比 315.33±66.46），视网膜厚度较薄（285.55±10.89 比 288.03±10.31）（均 P<0.05）。
结论
FTD 可作为定量生物标志物，用于估计儿童黄斑中心凹下脉络膜厚度。视盘内血流在 FT 进展中的作用需要进一步研究。FT 的分布和视盘旁模式与近视相关的眼底改变的相关性比黄斑模式更密切。
翻译
李育林
Zotero 插件