Dai Yanan, Zhu Pengxiong, Xie Yunhao, Xue Bangde, Ling Yun, Shi Xibao, Geng Liang, Hu Jian-Qiang, Zhang Qi, Liu Jun
Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, China.
State Key Laboratory of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
Sci Rep. 2025 Apr 3;15(1):11413. doi: 10.1038/s41598-025-95640-4.
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.
冠状动脉造影（CA）图像序列在诊断冠状动脉疾病（CAD）方面的潜力在很大程度上被忽视了。我们的研究旨在揭示这些图像中蕴含的“序列价值”，并探索其在诊断中的应用方法。我们通过亚马逊土耳其机器人（Mechanical Turk）进行了一项调查，以评估序列恢复能力在指示CAD方面的有效性。此外，我们开发了一种自监督深度学习模型来自动评估这种能力。此外，我们通过不同地选择冠状动脉造影/模块进行统计分析，确保了结果的稳健性。我们的自监督深度学习模型在五折交叉验证中平均AUC达到80.1%，展示了对静态数据噪声的稳健性和效率，计算在30秒内完成。本研究通过冠状动脉造影中的序列价值揭示了CAD诊断的重要见解。我们成功地展示了利用这一潜力的方法，为该领域贡献了宝贵的知识。
