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<p>The potential of the sequence in Coronary Angiography (CA) frames for diagnosing coronary artery disease (CAD) has been largely overlooked. Our study aims to reveal the “Sequence Value” embedded within these frames and to explore methods for its application in diagnostics. We conduct a survey via Amazon Mturk (Mechanical Turk) to evaluate the effectiveness of Sequence Restoration Capability in indicating CAD. Furthermore, we develop a self-supervised deep learning model to automatically assess this capability. Additionally, we ensure the robustness of our results by differently selecting coronary angiographies/modules for statistical analysis. Our self-supervised deep learning model achieves an average AUC of 80.1% across five-fold validation, demonstrating robustness against static data noise and efficiency, with calculations completed within 30&#xa0;s. This study uncovers significant insights into CAD diagnosis through the sequence value in coronary angiography. We successfully illustrate methodologies for harnessing this potential, contributing valuable knowledge to the field.</p>

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Linking sequence restoration capability of shuffled coronary angiography to coronary artery disease diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月3日

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  • Yanan Dai,
  • Pengxiong Zhu,
  • Yunhao Xie,
  • Bangde Xue,
  • Yun Ling,
  • Xibao Shi,
  • Liang Geng,
  • Jian-Qiang Hu,
  • Qi Zhang,
  • Jun Liu

摘要

The potential of the sequence in Coronary Angiography (CA) frames for diagnosing coronary artery disease (CAD) has been largely overlooked. Our study aims to reveal the “Sequence Value” embedded within these frames and to explore methods for its application in diagnostics. We conduct a survey via Amazon Mturk (Mechanical Turk) to evaluate the effectiveness of Sequence Restoration Capability in indicating CAD. Furthermore, we develop a self-supervised deep learning model to automatically assess this capability. Additionally, we ensure the robustness of our results by differently selecting coronary angiographies/modules for statistical analysis. Our self-supervised deep learning model achieves an average AUC of 80.1% across five-fold validation, demonstrating robustness against static data noise and efficiency, with calculations completed within 30 s. This study uncovers significant insights into CAD diagnosis through the sequence value in coronary angiography. We successfully illustrate methodologies for harnessing this potential, contributing valuable knowledge to the field.