Huang Dan, Lin Xiao, Zhu Hui, Ling Saiguang, Dong Zhou, Ke Xin, Long Tengfei, Qian Yingxiao, Yan Qi, Li Rui, Zhong Hua, Liu Hu
Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
Tongren Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Transl Vis Sci Technol. 2025 Jan 2;14(1):4. doi: 10.1167/tvst.14.1.4.
To evaluate the refractive differences among school-aged children with macular or peripapillary fundus tessellation (FT) distribution patterns, using fundus tessellation density (FTD) quantified by deep learning (DL) technology.
The cross-sectional study included 1942 school children aged six to 15 years, undergoing ocular biometric parameters, cycloplegic refraction, and fundus photography. FTD was quantified for both the macular (6 mm) and peripapillary (4 mm) regions, using DL-based image processing applied to 45° color fundus photographs. Eyes exhibiting tessellation were classified into two groups: the macular distribution group had greater FTD in the macular area, while the peripapillary distribution group had higher FTD in the peripapillary area, allowing for a comparative analysis of axial length (AL), corneal radius, and refraction.
Participants had a median age of 13 years and a median spherical equivalent (SE) of -0.75 D. The macular distribution group exhibited significantly larger AL (24.13 mm vs. 23.93 mm, P < 0.001) and more myopic refraction (-1.13 D vs. -0.75 D, P < 0.001) compared to the peripapillary group. A higher prevalence of macular-distributed FT was noted in the myopic groups (χ2 = 131.675, P < 0.001). SE negatively correlated with macular (r = -0.238) and peripapillary FTD (r = -0.195), while AL positively correlated with FTD in both regions (r = 0.308; r = 0.265) (all P < 0.001).
The macular FT distribution pattern is significantly associated with larger AL and greater myopic refraction in school-aged children, suggesting its potential as a marker for identifying children at risk of progressing myopia.
DL analysis precisely identifies FT distribution patterns, potentially enhancing early detection of high-risk myopia in populations.
利用深度学习(DL)技术量化的眼底镶嵌密度(FTD),评估黄斑或视盘周围眼底镶嵌(FT)分布模式的学龄儿童的屈光差异。
这项横断面研究纳入了1942名6至15岁的学龄儿童,他们接受了眼部生物测量参数、睫状肌麻痹验光和眼底照相。使用基于DL的图像处理技术对45°彩色眼底照片进行分析,量化黄斑区(6mm)和视盘周围区(4mm)的FTD。出现镶嵌的眼睛被分为两组:黄斑分布组黄斑区FTD较高,而视盘周围分布组视盘周围区FTD较高,从而可以对眼轴长度(AL)、角膜半径和屈光进行比较分析。
参与者的中位年龄为13岁,中位等效球镜(SE)为-0.75D。与视盘周围组相比,黄斑分布组的AL明显更长(24.13mm对23.93mm,P<0.001),近视屈光更多(-1.13D对-0.75D,P<0.001)。近视组中黄斑分布的FT患病率更高(χ2=131.675,P<0.001)。SE与黄斑FTD(r=-0.238)和视盘周围FTD(r=-0.195)呈负相关,而AL与两个区域的FTD均呈正相关(r=0.308;r=0.265)(所有P<0.001)。
黄斑FT分布模式与学龄儿童更长的AL和更高的近视屈光显著相关,表明其有可能作为识别有近视进展风险儿童的标志物。
DL分析可精确识别FT分布模式,有可能加强对人群中高危近视的早期检测。