Deep learning-based treatment decision support framework for multi-vessel coronary artery disease using integrated
Byeolhee Kim1, Junhee Kim2, Young-Hak Kim3
1Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Olympic-Ro 43-Gil, Seoul, 05505, Republic of Korea.
Insights
A new deep learning framework aids treatment decisions for multi-vessel coronary artery disease by analyzing coronary angiography and clinical data. This AI approach offers objective, data-driven support for revascularization choices, improving consistency and efficiency.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging Analysis
Background:
- Percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) selection for multi-vessel coronary artery disease is complex.
- Requires balancing anatomical and clinical patient factors for optimal treatment.
Purpose of the Study:
- To develop a deep learning framework for automated analysis of coronary angiography videos and clinical data.
- To support revascularization decision-making in multi-vessel coronary artery disease.
Main Methods:
- A three-module deep learning framework: video quality filtering, curriculum learning-based representative frame selection, and treatment classification.
- Integration of imaging features with clinical characteristics for decision support.
- Evaluation on 5,647 patient cases using 5-fold cross-validation.
Main Results:
- The framework achieved a mean AUC of 0.8275 ± 0.0167, significantly outperforming traditional machine learning.
- Ablation studies confirmed performance gains from frame selection (3.69%), video filtering (0.56%), and clinical data integration (1.35%).
- Curriculum learning for frame selection improved AUC by 6.3% compared to supervised learning.
Conclusions:
- The developed deep learning framework offers a promising, objective, data-driven approach for complex coronary revascularization decisions.
- Multi-modal integration and automated analysis enhance consistency and efficiency in treatment selection.
- Potential to improve clinical care standards in complex coronary artery disease management.
Background:
Treatment 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.
Methods:
We 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.
Results:
Our 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).
Conclusions:
This 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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