Abstract
BACKGROUND AND AIMS: Metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with coronary artery disease (CAD), but existing risk assessment tools lack precision and scalability. We developed an AI-driven multimodal framework integrating traditional Chinese medicine (TCM) tongue-based diagnosis with clinical biomarkers to stratify CAD risk in MAFLD. METHODS: In this cross-sectional study with prospective data collection, which comprised 1073 MAFLD patients stratified by CAD status (MAFLD without CAD, n = 942; MAFLD with CAD, n = 131), baseline characteristics were compared using chi-square tests for categorical variables and Mann-Whitney U tests for continuous variables, with p < 0.05 considered significant. We developed two distinct deep learning models: Model-1 (non-invasive) using ResNet18 combined tongue image features with demographic and comorbidity data, and Model-2 (comprehensive) incorporated blood biomarkers alongside the features used in Model-1. Multimodal feature fusion was achieved through a dedicated cross-attention network. Model performance was rigorously evaluated using 10-fold cross-validation, with area under the curve (AUC), sensitivity, and specificity as primary metrics. Focal loss was employed to address class imbalance. RESULTS: The coexistence of CAD was associated with significantly higher rates of hypertension, diabetes, and familial cardiovascular history in patients with MAFLD (p < 0.001), along with distinct metabolic profiles: elevated systemic inflammation (neutrophil-lymphocyte ratio), advanced fibrosis (FIB-4), elevated fasting glucose levels, and attenuated hepatic inflammation (ALT and AST). Model-1 achieved an AUC of 0.858 (sensitivity 0.778, specificity 0.908), while Model-2 demonstrated superior discrimination (AUC 0.933), particularly in tertiary care. CONCLUSION: Our AI-driven dual-model framework addresses the critical unmet need for CAD identification in MAFLD patients, providing a community-scalable screening tool (Model-1) and a precision clinical assessment model (Model-2), while offering empirical support for TCM tongue diagnosis. Pending external validation to confirm its generalizability, this approach may serve as a cost-efficient non-invasive screening strategy for this high-risk population.