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.

在数字医学时代，医学影像作为一种广泛应用的早期疾病检测技术，在每天产生和储存的电子病历中产生大量的影像。在这篇论文中，我们利用目标检测方法对 X 射线血管造影图像进行分析，精确地确定冠状动脉狭窄的位置，从而实现了对狭窄位置的自动实时检测，帮助医护人员进行重要而敏感的决策过程。