Department of Ophthalmology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan.
Department of Ophthalmology, Seirei Hamamatsu General Hospital, Shizuoka, Japan.
Graefes Arch Clin Exp Ophthalmol. 2023 Aug;261(8):2411-2419. doi: 10.1007/s00417-023-06024-1. Epub 2023 Mar 1.
Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex of an older individual can be determined by regression analysis of their color fundus photographs (CFPs).
Forty-two parameters were analyzed by regression analysis using 1653 CFPs of normal subjects in the Kumajima study. The parameters included the mean values of red, green, and blue intensities; the tessellation fundus index; the optic disc ovality ratio; the papillomacular angle; and the retinal vessel angles. Finally, the L2 regularized binomial logistic regression was used to predict the sex using all the parameters, and the diagnostic ability was assessed through the leave-one-cross-validation.
The mean age of the 838 men and 815 women were 52.8 and 54.0 years, respectively. The ovality ratio and retinal artery angles in women were significantly smaller than that in men. The green intensity at all locations for the women were significantly higher than that of men (P < 0.001). The discrimination accuracy rate assessed by the area-under-the-curve was 80.4%.
Our methods can determine the sex from the CFPs of the adult with an accuracy of 80.4%. The ovality ratio, retinal vessel angles, tessellation, and the green intensities of the fundus are important factors to identify the sex in individuals over 40 years old.
深度学习人工智能仅通过眼底照片就能确定性别。然而，深度学习用来确定性别的因素是不可见的。因此，本研究的目的是通过对久留米研究中 1653 例正常受试者的眼底彩色照片（CFPs）进行回归分析，来确定是否可以通过分析老年人的 CFPs 来确定其性别。
使用回归分析对 1653 例久留米研究中正常受试者的 CFPs 分析了 42 个参数。这些参数包括红色、绿色和蓝色强度的平均值；网格眼底指数；视盘椭圆率比；乳头黄斑角；以及视网膜血管角度。最后，使用所有参数的 L2 正则化二项逻辑回归来预测性别，并通过留一交叉验证评估诊断能力。
838 名男性和 815 名女性的平均年龄分别为 52.8 岁和 54.0 岁。女性的椭圆率比和视网膜动脉角度明显小于男性。女性所有位置的绿色强度均明显高于男性（P<0.001）。通过曲线下面积评估的判别准确率为 80.4%。
我们的方法可以通过 CFPs 以 80.4%的准确率确定成年人的性别。在 40 岁以上的个体中，椭圆率比、视网膜血管角度、网格和眼底的绿色强度是识别性别的重要因素。
