ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
This article is part of the Research TopicExplainable Artificial Intelligence for Trustworthy and Human‑Centric Healthcare: Methods, Evaluation, and Clinical ImpactView all articles
An Explainable Dual-Modal Diagnostic Model for Coronary Artery Disease: A Feature-Gated Approach Using Tongue and Facial Image Features
Provisionally accepted
- 1Beijing University of Chinese Medicine, Beijing, China
- 2Tsinghua University, Beijing, China
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Background and Objective :Coronary artery disease (CAD) is a major threat to human health, and early non-invasive identification is crucial for its prevention and management. However, current diagnostic methods still face limitations in terms of non-invasiveness, cost, and accessibility. Tongue and facial features have been recognized as closely associated with CAD. To address these challenges, this study proposes a dual-modal diagnostic model incorporating a feature-wise gating mechanism to enable intelligent, non-invasive CAD detection based on tongue and facial images. Methods: A total of 936 participants were enrolled in this study, and standardized tongue and facial images were collected from each subject. Image segmentation was performed using MedSAM, followed by deep semantic feature extraction using the MDFA-Swin network. Traditional color and texture features were also incorporated. A feature-guided gating mechanism was developed to enable personalized multimodal fusion of tongue and facial features. The diagnostic performance of the proposed model was evaluated on an independent external test set. In addition, SHAP (SHapley Additive Explanations) analysis were conducted to enhance model interpretability. Results:The proposed CAD diagnostic model based on fused multidimensional tongue and facial features (TF_FGC) demonstrated excellent performance in internal validation (AUC = 0.945, Accuracy = 0.872) and maintained good generalizability on the external test set (AUC = 0.896, Accuracy = 0.825). The SHAP analysis identified T_contrast, T_RGB_R, T_homogeneity, F_homogeneity, F_RGB_B, F_RGB_G, F_RGB_R, and F_contrast as the most influential features driving model predictions. Conclusion:The proposed dual-branch fusion model demonstrates high diagnostic accuracy, strong interpretability, and good generalizability. By integrating traditional color and texture features with deep semantic representations, this approach offers a promising solution for non-invasive and
Keywords: Coronary Artery Disease, Diagnostic model, tongue feature, Facial feature, Feature-wise Gating Mechanism, explainable artificial intelligence (XAI)
Received: 09 Jul 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Zhao, Wang, Fan, Liu, Wei, Dong and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaoqing Zhang, 202001003@bucm.edu.cn
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