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Exploiting the Retinal Vascular Geometry in Identifying the Progression to Diabetic Retinopathy Using Penalized Logistic Regression and Random Forests

机译：利用惩罚物流回归和随机森林鉴定视网膜血管几何形状鉴定糖尿病视网膜病变的进展

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Many studies have been conducted, investigating the effects that diabetes has to the retinal vasculature. Identifying and quantifying the retinal vascular changes remains a very challenging task, due to the heterogeneity of the retina. Monitoring the progression requires follow-up studies of progressed patients, since human retina naturally adapts to many different stimuli, making it hard to associate any changes with a disease. In this novel study, data from twenty five diabetic patients, who progressed to diabetic retinopathy, were used. The progression was evaluated using multiple geometric features, like vessels widths and angles, tortuosity, central retinal artery and vein equivalent, fractal dimension, lacunarity, in addition to the corresponding descriptive statistics of them. A statistical mixed model design was used to evaluate the significance of the changes between two periods: 3 years before the onset of diabetic retinopathy and the first year of diabetic retinopathy. Moreover, the discriminative power of these features was evaluated using a random forests classifier and also a penalized logistic regression. The area under the ROC curve after running a ten-fold cross validation was 0.7925 and 0.785 respectively.
机译：已经进行了许多研究，研究了糖尿病对视网膜脉管系统的影响。由于视网膜的异质性，鉴定和量化视网膜血管变化仍然是一个非常具有挑战性的任务。监测进展需要对进取的患者进行后续研究，因为人视网膜自然适应许多不同的刺激，使得难以将任何变化与疾病联系起来。在这项新建的研究中，使用了来自二十五名糖尿病患者的数据，用于糖尿病视网膜病变。除了与它们的相应描述性统计之外，使用多个几何特征，如血管宽度和角度，曲折，中央视网膜动脉和静脉等程，分形维度，曲线性，除了它们的相应描述性统计之外，还使用多个几何特征评估进展。统计混合模型设计用于评估两个时期之间变化的重要性：糖尿病视网膜病变前3年和糖尿病视网膜病变的第一年。此外，使用随机森林分类器和惩罚的逻辑回归评估这些特征的辨别力。运行十倍交叉验证后ROC曲线下的区域分别为0.7925和0.785。

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