Mengyao Duan ¹ Boyan Mao ² Zijian Li ¹ ChuHao Wang ² Zhixi Hu ^3* Jing Guan ^1* Feng Li ^1*

• ¹ School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
• ² School of Life Sciences, Beijing University of Chinese Medicine, Beijing, Beijing, China
• ³ School of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Anhui Province, China

Aim: Clarify the potential diagnostic value of tongue images for coronary artery disease (CAD), develop a CAD diagnostic model that enhances performance by incorporating tongue image inputs, and provide more reliable evidence for the clinical diagnosis of CAD, offering new biological characterization evidence.Method: We recruited 684 patients from four hospitals in China for a cross-sectional study, collecting their baseline information and standardized tongue images to train and validate our CAD diagnostic algorithm. We used DeepLabV3+ for segmentation of the tongue body and employed Resnet-18, pretrained on ImageNet, to extract features from the tongue images. We applied DT (Decision Trees), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), and XGBoost models, developing CAD diagnostic models with inputs of risk factors alone and then with the additional inclusion of tongue image features. We compared the diagnostic performance of different algorithms using accuracy, precision, recall, F1-score, AUPR , and AUC.Result: We classified patients with CAD using tongue images and found that this classification criterion was effective (ACC=0.670, AUC=0.690, Recall=0.666). After comparing algorithms such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and XGBoost, we ultimately chose XGBoost to develop the CAD diagnosis algorithm. The performance of the CAD diagnosis algorithm developed solely based on risk factors was ACC=0.730, Precision=0.811, AUC=0.763. When tongue features were integrated, the performance of the CAD Running Title 2 diagnosis algorithm improved to ACC=0.760, Precision=0.773, AUC=0.786, Recall=0.850, indicating an enhancement in performance. Conclusion: The use of tongue images in the diagnosis of CAD is feasible, and the inclusion of these features can enhance the performance of existing CAD diagnosis algorithms. We have customized this novel CAD diagnosis algorithm, which offers the advantages of being noninvasive, simple, and cost-effective. It is suitable for large-scale screening of CAD among hypertensive populations. Tongue image features may emerge as potential biomarkers and new risk indicators for CAD.