Detection of Obstructive Coronary Artery Disease Using a Deep Learning and Machine Learning Ensemble: A Retrospective Feasibility Study

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ID: 325101
2026
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Abstract
Abstract Aims Early identification of obstructive coronary artery disease (ObCAD) is crucial because it is strongly associated with acute myocardial infarction. We developed a weighted average ensemble model integrating deep learning (DL) and machine learning (ML) to leverage imaging and clinical data for enhancing the detection of ObCAD. Methods and results A retrospective cohort of 1,054 patients was used to develop an ensemble model combining a 3D Vision Transformer (ViT) with eXtreme Gradient Boosting and CatBoost for binary classification of ObCAD (>50% stenosis). Unstructured data comprised 3D cardiac non-contrast computed tomography scans, while structured data included 11 demographic and clinical features. ObCAD labels were derived from corresponding coronary computed tomography angiography. Model performance was evaluated using 10-fold cross-validation with fold-wise Wilcoxon signed-rank testing. The ensemble model achieved a mean area under the receiver operating characteristic curve (ROC AUC) of 0.81 ± 0.04 and an accuracy of 0.76 ± 0.04. It demonstrated a statistically significantly higher ROC AUC than individual component models. Feature importance analysis identified age, chest pain, and sex as the most influential predictors of ObCAD. Grad-CAM visualization indicated that the 3D ViT primarily focused on cardiac regions containing coronary artery calcium deposits. Conclusion Integrating DL-based imaging analysis with ML-based clinical modeling enhances the discriminative performance for ObCAD detection with complementary interpretability. This ensemble framework demonstrates potential to support clinical decision-making by identifying high-risk patients using routine cardiac CT combined with patient-level clinical data. Future studies using external validation and coronary artery calcium scores may further improve risk prediction.
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Authors Doyoung Park, Linxuan Yan, Arman Ahmad Khan, Wilbert Hsien Hao Ho, Choon Ta Ng, Jonathan Yap, Swee Yaw Tan, Khung Keong Yeo, Lohendran Baskaran
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Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag134
URL
https://doi.org/10.1093/ehjdh/ztag134
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