北京中医药大学学报2025，Vol.48Issue(12)：1705-1714,10.DOI:10.3969/j.issn.1006-2157.2025.12.009
基于舌象及深度学习构建冠状动脉病变严重程度的分类模型
Constructing a classification model for coronary artery lesion severity based on tongue manifestations and deep learning
摘要
Abstract
Objective To develop a deep learning-based intelligent tongue manifestation recognition model for the noninvasive assessment of coronary artery disease severity.Methods This study prospectively enrolled 147 patients with coronary heart disease who were hospitalized in the Department of Cardiology at Dongzhimen Hospital,Beijing University of Chinese Medicine.General patient information was collected,including gender,age,past history,laboratory test result,and medication use.According to the Gensini score system,the severity of coronary artery lesions was stratified into mild group(n=44),moderate group(n=53),and severe group(n=50);and 147 patients were randomly divided into the training set(n=103)and the test set(n=44)at a ratio of 7∶3 using the random nomber table method.Standardized tongue images were acquired using the Daosheng DS01-B tongue and facial diagnosis information acquisition system.The improved UNet++model(ISE-UNet++),incorporating multi-scale convolution and channel attention mechanisms,was employed for precise segmentation of the tongue region.A residual neural network(ResNet)deep learning model was then constructed to classify the severity of coronary lesions based on tongue images.Finally,gradient-weighted class activation mapping(Grad-CAM)was used to visualize the discriminative regions of tongue images identified by the deep learning model across different severities of coronary artery disease.Results A total of 147 patients with coronary heart disease were included in this study,among whom 73 were male(49.7%)and 74 were female(50.3%),with an average age of(73.05±8.24)years.In the tongue image segmentation task,the ISE-UNet++model outperformed the original UNet++model,showing improvements in mean intersection over union(MIoU),mean pixel accuracy(MPA),and overall accuracy.For the classification of coronary artery disease(CAD)severity,the ResNet-50 model demonstrated the best performance on both the training and test sets.Specifically,the sensitivity was 84.8%and 74.5%,specificity was 0.829 and 0.756,precision was 0.741 and 0.788,recall was 0.805 and 0.819,F1-score was 0.790 and 0.813,accuracy was 0.809 and 0.778,Kappa coefficient was 0.777 and 0.715,and AUC was 0.880 and 0.854,respectively—all metrics surpassing those of ResNet-18 and ResNet-34 models.Grad-CAM visualization analysis revealed that in patients with mild coronary lesions,the model's attention was focused on the tongue tip,whereas in more severe cases,the attention shifted progressively toward the middle and root regions of the tongue.Conclusion The combination of deep learning and tongue image analysis offers a novel noninvasive approach for assessing coronary artery disease severity,with promising potential for clinical application.关键词
冠心病/舌象/深度学习/梯度加权类激活映射Key words
coronary heart disease/tongue manifestation/deep learning/gradient-weighted class activation mapping分类
医药卫生引用本文复制引用
魏东升,赵梅,赵梦兰,顾文豪,柴泽龙,张晓晴..基于舌象及深度学习构建冠状动脉病变严重程度的分类模型[J].北京中医药大学学报,2025,48(12):1705-1714,10.基金项目
国家重点研发计划项目(No.2022YFC3502300,No.2022YFC3502301) National Key R&D Program of China(Nos.2022YFC3502300 and 2022YFC3502301) （No.2022YFC3502300,No.2022YFC3502301）
