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

Figures

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  Fig. 1. Overview of the deep learning framework for multi-vessel coronary artery disease treatment decision support. Starting with patient CAG videos (Video\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}_1$$\end{document}, Video\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}_2$$\end{document}, …, Video\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}_n$$\end{document}), the framework processes data through three sequential modules: (A) Video quality filtering module employs a ResNet-50 backbone with LCA/RCA classifier head to assess video quality through vessel classification confidence, retaining only videos with mean probability ≥0.95 across…

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  Fig. 2. Patient selection flowchart showing filtering process from initial cohort to final study population. Each step indicates specific inclusion/exclusion criteria applied

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  Fig. 3. Overview of the semi-supervised curriculum learning process with pseudo labeling. The process begins with labeled data training, followed by pseudo-label generation for unlabeled data. The curriculum strategy gradually incorporates pseudo-labeled samples based on confidence scores, with periodic model updates every 5 epochs

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  Fig. 4. Visualization of model attention using Grad-CAM for different clinical scenarios. Each pair shows the original coronary angiogram frame (left) and its corresponding activation map (right), where red regions indicate areas of high attention that influenced the model’s decision-making process. The activation patterns consistently highlight clinically significant anatomical features that typically influence treatment decisions between PCI and CABG

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  Fig. 5. Left: mean ROC curve for proposed model with cross validation(k = 5) right: comparison of mean ROC curves with machine learning models