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Background <p>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.</p> Methods <p>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.</p> Results <p>Our framework demonstrated superior performance with a mean AUC of 0.8275 <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\pm\)</EquationSource></InlineEquation> 0.0167 in 5-fold cross-validation, significantly outperforming traditional machine learning approaches (baseline AUC: 0.66 <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\pm\)</EquationSource></InlineEquation> 0.007, <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(p &lt; 0.001\)</EquationSource></InlineEquation>). 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).</p> Conclusions <p>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.</p> Clinical trial number <p>Not applicable.</p>

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Deep learning-based treatment decision support framework for multi-vessel coronary artery disease using integrated coronary angiography and clinical data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Byeolhee Kim,
  • Junhee Kim,
  • Young-Hak Kim,
  • Tae Joon Jun,
  • Jung-Min Ahn

摘要

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 \(\pm\) 0.0167 in 5-fold cross-validation, significantly outperforming traditional machine learning approaches (baseline AUC: 0.66 \(\pm\) 0.007, \(p < 0.001\)). 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.