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。

基于深度学习的自动冠状动脉物体检测

Object Detection for Automated Coronary Artery Using Deep Learning

The dawn of the digital medicine era, ushered in by increasingly powerful
embedded systems and Internet of Things (IoT) computing devices, is creating
new therapies and biomedical solutions that promise to positively transform our
quality of life. However, the digital medicine revolution also creates
unforeseen and complex ethical, regulatory, and societal issues. In this
article, we reflect on the ethical challenges facing digital medicine. We
discuss the perils of ethical oversights in medical devices, and the role of
professional codes and regulatory oversight towards the ethical design,
deployment, and operation of digital medicine devices that safely and
effectively meet the needs of patients. We advocate for an ensemble approach of
intensive education, programmable ethical behaviors, and ethical analysis
frameworks, to prevent mishaps and sustain ethical innovation, design, and
lifecycle management of emerging digital medicine devices.

本文讨论数字医学面临的伦理挑战，强调医疗器械的伦理设计、部署和操作的重要性，并提倡利用教育、可编程的伦理行为和伦理分析框架等方法来预防错误和持续推动数字医学设备的伦理创新、设计和生命周期管理。