Feasibility of tongue image detection for coronary artery disease: based on deep learning

Abstract

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.

Methods: 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.

Results: 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 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.

Keywords: coronary artery disease; deep learning; early diagnosis; hypertension; tongue image.

Figure 1

The tongue diagnosis instrument and collection process. 1: lens hood, 2: LED light resource, 3: high-definition camera, 4: chin support plate. Note. Use fixed standard camera parameters when shooting: the color temperature is 5,000 k, the color rendering index is 97, the frame rate is 1/125 s, the aperture is F/6.3, the exposure indicator scale is 0 or ±1. (A) Front view of the device; (B) Side view of the device; (C) Rear view of the device; (D) Schematic of the image acquisition process; (E) Perspective of the operator.

Figure 2

Data preprocessing for tongue images. (A) DeepLabV3 + framework diagram, (B) model training loss function graph, (C) preprocessing effect on tongue image.

Figure 3

Tongue image-based CAD diagnostic algorithm. (A) ResNet-18 framework used in this study, (B) 5-fold cross-validation mean ROC on training set, (C) 5-fold cross-validation mean ROC Comparison on training set, (D) performance comparison of different feature inputs on the validation set.

Figure 4

Study flowchart. (A) Workflow for algorithm development; (B) Workflow for algorithm validation.

Figure 5

Algorithm performance in subgroups of test group. (A) AUC for the model in individuals under 65 years old; (B) AUC for the model in individuals 65 years and older; (C) AUC for the model in males; (D) AUC for the model in females; (E) AUC for the model in individuals with fewer than 3 risk factors; (F) AUC for the model in individuals with 3 or more risk factors.