Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Department of Neurosurgery, Tsinghua University Yuquan Hospital, Beijing, China.
Transl Vis Sci Technol. 2021 Aug 2;10(9):23. doi: 10.1167/tvst.10.9.23.
This study aimed to quantitative assess the fundus tessellated density (FTD) and associated factors on the basis of fundus photographs using artificial intelligence.
A detailed examination of 3468 individuals was performed. The proposed method for FTD measurements consists of image preprocessing, sample labeling, deep learning segmentation model, and FTD calculation. Fundus tessellation was extracted as region of interest and then the FTD could be obtained by calculating the average exposed choroid area of per unit area of fundus. Besides, univariate and multivariate linear regression analysis have been conducted for the statistical analysis.
The mean FTD was 0.14 ± 0.08 (median, 0.13; range, 0-0.39). In multivariate analysis, FTD was significantly (P < 0.001) associated with thinner subfoveal choroidal thickness, longer axial length, larger parapapillary atrophy, older age, male sex and lower body mass index. Correlation analysis suggested that the FTD increased by 33.1% (r = 0.33, P < .001) for each decade of life. Besides, correlation analysis indicated the negative correlation between FTD and spherical equivalent (SE) in the myopia participants (r = -0.25, P < 0.001), and no correlations between FTD and SE in the hypermetropia and emmetropic participants.
It is feasible and efficient to extract FTD information from fundus images by artificial intelligence-based imaging processing. FTD can be widely used in population screening as a new quantitative biomarker for the thickness of the subfoveal choroid. The association between FTD with pathological myopia and lower visual acuity warrants further investigation.
Artificial intelligence can extract valuable clinical biomarkers from fundus images and assist in population screening.
本研究旨在基于人工智能利用眼底照片定量评估眼底镶嵌密度（FTD）及其相关因素。
对 3468 人进行了详细检查。FTD 测量的方法包括图像预处理、样本标记、深度学习分割模型和 FTD 计算。提取眼底镶嵌作为感兴趣区域，然后通过计算单位面积眼底暴露脉络膜的平均面积来获得 FTD。此外，还进行了单变量和多变量线性回归分析进行统计分析。
平均 FTD 为 0.14±0.08（中位数，0.13；范围，0-0.39）。多变量分析显示，FTD 与较薄的中心凹下脉络膜厚度、较长的眼轴长度、较大的视盘周围萎缩、较大年龄、男性和较低的体重指数显著相关（均 P<0.001）。相关性分析表明，FTD 随年龄增长增加 33.1%（r=0.33，P<0.001）。此外，相关性分析表明，近视患者的 FTD 与等效球镜（SE）呈负相关（r=-0.25，P<0.001），而远视和正视患者的 FTD 与 SE 无相关性。
基于人工智能的成像处理从眼底图像中提取 FTD 信息是可行且高效的。FTD 可广泛应用于人群筛查，作为中心凹下脉络膜厚度的新定量生物标志物。FTD 与病理性近视和较低视力之间的关联需要进一步研究。
Leona
