ArticleBMC medical informatics and decision making2026
Deep learning-based treatment decision support framework for multi-vessel coronary artery disease using integrated coronary angiography and clinical data.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTreatment selection between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for multi-vessel coronary artery disease remains challenging, requiring careful consideration of both anatomical and clinical factors.
methodsWe developed a deep learning framework that automatically analyzes coronary angiography videos and integrates clinical data to support revascularization decisions. The framework consists of three key modules: (1) a video filtering module for quality screening, (2) a representative frame selection module based on curriculum learning, and (3) a treatment classification module combining imaging features with clinical characteristics. The framework was evaluated using 5,647 patients' data from a single center, with cross-validation.
resultsOur framework demonstrated superior performance with a mean AUC of 0.8275 ± 0.0167 in 5-fold cross-validation, significantly outperforming traditional machine learning approaches (baseline AUC: 0.66 ± 0.007, [Formula: see text]). Ablation studies showed sequential improvements: representative frame selection improved performance over baseline by 3.69% (AUC: 0.6657 to 0.7026), video quality filtering provided additional 0.56% improvement (AUC: 0.7026 to 0.7082), and clinical information integration achieved final enhancement of 1.35% (AUC: 0.7082 to 0.7217). For frame selection specifically, curriculum learning outperformed supervised learning by 6.3% (AUC: 0.9067 to 0.9637).
conclusionsThis study provides a promising approach for objective, data-driven decision support in complex coronary revascularization cases. The framework's multi-modal integration strategy and automated analysis capabilities demonstrate potential for improving the consistency and efficiency of treatment selection while maintaining high standards of clinical care. CLINICAL TRIAL NUMBER: Not applicable.
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