AI-assisted segmentation-based fusion model integrating deep learning, radiomics, and clinical parameters for identifying coronary heart disease risk in patients with MAFLD

Background/objectives Metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with an increased risk of coronary heart disease (CHD), which is one of the leading causes of chronic disease-related mortality worldwide. Early identification of CHD risk in patients with MAFLD is essential for risk stratification and timely intervention. Although radiomics and deep learning (DL) have shown promising performance in medical image analysis, their application for CHD risk assessment in patients with MAFLD remains limited. Therefore, this study aimed to develop and validate an AI-assisted segmentation-based deep learning radiomics-clinical (DLRC) model for identifying CHD risk in patients with MAFLD. Methods A total of 1,515 patients with MAFLD, including 440 with concomitant CHD, were retrospectively enrolled between January 2023 and December 2025. Patients were randomly divided into a training cohort (n = 1,060) and a test cohort (n = 455). AI-assisted liver segmentation was performed using a deep learning-assisted framework integrated into ITK-SNAP. Radiomics features were extracted using PyRadiomics, and DL features were extracted using a pre-trained DenseNet-121 network. After feature selection using Pearson correlation analysis, minimum redundancy maximum relevance (mRMR), principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) regression, radiomics and DL features were integrated to construct a deep learning radiomics (DLR) model. Clinical predictors were identified using multivariable logistic regression. Clinical, radiomics, DLR, and combined DLRC models were developed and evaluated using machine learning algorithms. Model performance was assessed by receiver operating characteristic (ROC) analysis, calibration curves, DeLong tests, and decision curve analysis (DCA). Results Multivariable analysis identified older age, hypertension, diabetes mellitus, hyperlipidemia, male sex, and lower BMI as independent predictors of CHD in patients with MAFLD. Eleven radiomics features and fifteen DLR features were selected for model construction. The DLRC model achieved the best predictive performance, with AUCs of 0.917 in the training cohort and 0.878 in the test cohort, outperforming the DLR model (0.895 and 0.854), radiomics model (0.821 and 0.784), clinical model (0.768 and 0.751), respectively. DeLong tests demonstrated significant superiority of the DLRC model over the other models (all P < 0.05). Calibration and DCA analyses further confirmed its excellent calibration and clinical utility. Conclusion An AI-assisted segmentation-based fusion model integrating clinical factors, radiomics features, and DL features demonstrated excellent performance for identifying CHD risk in patients with MAFLD. This non-invasive and efficient approach may facilitate early risk stratification and personalized management of MAFLD patients.