[A deep-learning model for the assessment of coronary heart disease and related risk factors via the evaluation of retinal fundus photographs].

Objective: To develop and validate a deep learning model based on fundus photos for the identification of coronary heart disease (CHD) and associated risk factors. Methods: Subjects aged>18 years with complete clinical examination data from 149 hospitals and medical examination centers in China were included in this retrospective study. Two radiologists, who were not aware of the study design, independently evaluated the coronary angiography images of each subject to make CHD diagnosis. A deep learning model using convolutional neural networks (CNN) was used to label the fundus images according to the presence or absence of CHD, and the model was proportionally divided into training and test sets for model training. The prediction performance of the model was evaluated in the test set using monocular and binocular fundus images respectively. Prediction efficacy of the algorithm for cardiovascular risk factors (e.g., age, systolic blood pressure, gender) and coronary events were evaluated by regression analysis using the area under the receiver operating characteristic curve (AUC) and R2 correlation coefficient. Results: The study retrospectively collected 51 765 fundus images from 25 222 subjects, including 10 255 patients with CHD, and there were 14 419 male subjects in this cohort. Of these, 46 603 fundus images from 22 701 subjects were included in the training set and 5 162 fundus images from 2 521 subjects were included in the test set. In the test set, the deep learning model could accurately predict patients' age with an R2 value of 0.931 (95%CI 0.929-0.933) for monocular photos and 0.938 (95%CI 0.936-0.940) for binocular photos. The AUC values for sex identification from single eye and binocular retinal fundus images were 0.983 (95%CI 0.982-0.984) and 0.988 (95%CI 0.987-0.989), respectively. The AUC value of the model was 0.876 (95%CI 0.874-0.877) with either monocular fundus photographs and AUC value was 0.885 (95%CI 0.884-0.888) with binocular fundus photographs to predict CHD, the sensitivity of the model was 0.894 and specificity was 0.755 with accuracy of 0.714 using binocular fundus photographs for the prediction of CHD. Conclusion: The deep learning model based on fundus photographs performs well in identifying coronary heart disease and assessing related risk factors such as age and sex.目的： 开发并验证一款基于视网膜眼底图像的深度学习模型，用于识别冠心病及其危险因素。 方法： 本研究为回顾性研究。收集2018年7月至2021年6月来自中国149家医院和体检中心，年龄>18岁、具有完整冠状动脉造影及视网膜眼底图像的受试者。2名对研究设计不知情的放射科医师独立评估每位受试者的冠状动脉造影图像，判断是否诊断为冠心病。使用卷积神经网络（CNN）深度学习模型，根据有无冠心病将视网膜眼底图像进行标注，按比例分为训练集和测试集进行模型训练。并且在测试集中分别使用单眼和双眼眼底图像评估模型预测性能。使用受试者工作特征曲线下面积（AUC）及相关系数（R2）评估模型识别心血管疾病危险因素（年龄、血压、性别）以及冠心病的效能。 结果： 本研究收集到25 222名受试者的51 765张眼底图像，其中男性14 419名，冠心病患者10 255例。训练集纳入了22 701名受试者的46 603张眼底图像，测试集共纳入2 521名受试者的5 162张眼底图像。在测试集中，模型从单眼和双眼视网膜眼底图像中判断年龄的R2分别为0.931（95%CI 0.929~0.933）和0.938（95%CI 0.936~0.940）。从单眼和双眼视网膜眼底图像中识别性别的AUC值分别为0.983（95%CI 0.982~0.984）和0.988（95%CI 0.987~0.989）。该模型运用单眼（任一）眼底照片识别冠心病的AUC值为0.876（95%CI 0.874~0.877），双眼（均值）眼底照片的AUC值为0.885（95%CI 0.884~0.888），模型通过双眼视网膜眼底照片判断冠心病的灵敏度为0.894，特异度为0.755，准确度为0.714。 结论： 基于视网膜眼底图像的深度学习模型在评估冠心病及其危险因素（年龄、性别）方面表现良好。.