目的:深度学习人工智能可以仅使用眼底照片来确定性别。然而,深度学习用来确定性别的因素是不可见的。因此,本研究的目的是确定年龄较大的个体的性别是否可以通过对其彩色眼底照片(CFPs)的回归分析来确定.
方法:使用Kumajima研究中正常受试者的1653CFP通过回归分析对42个参数进行分析。参数包括红色的平均值,绿色,和蓝色强度;细分眼底指数;视盘椭圆率;乳头状瘤角;和视网膜血管角。最后,L2正则化二项逻辑回归用于使用所有参数预测性别,诊断能力通过离开一交叉验证进行评估.
结果:838名男性和815名女性的平均年龄分别为52.8和54.0岁,分别。女性的椭圆率和视网膜动脉角度明显小于男性。女性在所有位置的绿色强度均显着高于男性(P<0.001)。通过曲线下面积评估的判别准确率为80.4%。
结论:我们的方法可以从成人的CFP中确定性别,准确率为80.4%。椭圆率,视网膜血管角度,镶嵌,眼底的绿色强度是确定40岁以上个体性别的重要因素。
OBJECTIVE: 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).
METHODS: 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.
RESULTS: 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%.
CONCLUSIONS: 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.