用眼底图预测心血管风险，模型对年龄和血压预测效果好。
Prediction of Cardiovascular Risk Factors from Retinal Fundus Images using CNNs
- 用CNN分析眼底图，结合左右眼图像提升预测精度。
- 年龄预测R²达0.81，收缩压预测R²达0.39，优于以往研究。
- 首次尝试预测糖化血红蛋白和总胆固醇，但效果有限。
早期发现心血管疾病风险因素对干预病情至关重要。已有研究表明深度学习可从眼底图像中识别这些风险因素。本研究利用卷积神经网络（CNN）从英国生物样本库（UK Biobank）数据集的眼底图像中预测年龄、体重指数（BMI）、吸烟状态、糖化血红蛋白（HbA1c）、收缩压、舒张压、性别和总胆固醇等风险因素。通过高斯滤波增强图像对比度，并融合左右眼预测结果进行个体化推断，使年龄预测的R²达到0.81，收缩压预测的R²达到0.39，优于此前使用该数据集的研究。此外，本研究首次尝试从英国生物样本库眼底图像中预测HbA1c和总胆固醇，分别获得R²为0.0579和0.0157的结果，表明其预测价值有限。
原文摘要 · Abstract (English)
Early detection of cardiovascular disease risk factors is essential to alter the course of the disease. Previous studies showed that deep learning can successfully be used to detect such risk factors from retinal images. This study uses convolutional neural networks (CNNs) to predict the cardiovascular disease risk factors age, BMI, smoking status, HbA1c, systolic blood pressure, diastolic blood pressure, gender and total cholesterol from retinal images from the UK Biobank data set. By applying contrast enhancement on the retinal images in the form of Gaussian filtering and deriving predictions on individual basis through the combination of left and right retinal image predictions, an increased prediction performance could be derived for the variables age (R2 score of 0.81) and systolic blood pressure (R2 score of 0.39) compared to previous studies using retinal images from the UK Biobank data set. Further, this is the first study that tries to predict HbA1c and total cholesterol from UK Biobank retinal fundus images. For these variables the models achieved an R2 score of 0.0579 for predicting HbA1c and an R2 score of 0.0157 for predicting total cholesterol. These results show that the value of deriving predictions for these two risk factors from retinal fundus images from the UK Biobank data set is limited.
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