Deep learning model based on fundus images for detection of coronary artery disease with mild cognitive impairment
10.3969/j.issn.1004-8812.2025.06.002
- VernacularTitle:基于眼底影像筛查冠心病合并轻度认知障碍患者的深度学习模型研究
- Author:
Yi YE
1
;
Wei FENG
;
Yao-dong DING
;
Qing CHEN
;
Yang ZHANG
;
Li LIN
;
Tong MA
;
Bin WANG
;
Xian-gang CHANG
;
Zong-yuan GE
;
Xiao-yi WANG
;
Long-jun CAI
;
Yong ZENG
Author Information
1. 首都医科大学附属北京安贞医院冠心病中心,北京 100029
- Publication Type:Journal Article
- Keywords:
Mild cognitive impairment;
Coronary heart disease;
Fundus images;
Deep learning
- From:
Chinese Journal of Interventional Cardiology
2025;33(6):303-311
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.
