In the era of digital medicine, medical imaging serves as a widespread technique for early disease detection, with a substantial volume of images being generated and stored daily in electronic patient records. X-ray angiography imaging is a standard and one of the most common methods for rapidly diagnosing coronary artery diseases. The notable achievements of recent deep learning algorithms align with the increased use of electronic health records and diagnostic imaging. Deep neural networks, leveraging abundant data, advanced algorithms, and powerful computational capabilities, prove highly effective in the analysis and interpretation of images. In this context, Object detection methods have become a promising approach, particularly through convolutional neural networks (CNN), streamlining medical image analysis by eliminating manual feature extraction. This allows for direct feature extraction from images, ensuring high accuracy in results. Therefore, in our paper, we utilized the object detection method on X-ray angiography images to precisely identify the location of coronary artery stenosis. As a result, this model enables automatic and real-time detection of stenosis locations, assisting in the crucial and sensitive decision-making process for healthcare professionals.

翻译：在数字医疗时代，医学影像作为早期疾病检测的广泛应用技术，每日有大量图像生成并存储于电子病历中。X射线血管造影成像是一种标准且最常用的快速诊断冠状动脉疾病的方法。近年来深度学习算法的显著成就与电子健康记录及诊断影像应用的增加相契合。深度神经网络凭借海量数据、先进算法和强大计算能力，在图像分析与解读中展现出卓越有效性。在此背景下，目标检测方法已成为一种极具前景的技术途径，特别是通过卷积神经网络（CNN），通过消除人工特征提取简化了医学图像分析流程。该方法可直接从图像中提取特征，确保结果的高精度。因此，本文采用目标检测方法对X射线血管造影图像进行处理，精确定位冠状动脉狭窄位置。该模型能够实现狭窄位置的自动实时检测，为医疗专业人员的关键敏感决策过程提供辅助支持。