Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000′s. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image.

In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology.

The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.

在过去的 30 年中，各个国家都采用了乳房 X 线摄影术筛查乳腺癌，最初使用基于模拟屏幕胶片的系统，在过去的 20 年中，过渡到使用全数字系统。随着数字化的引入，图像的计算机解释一直是人们非常感兴趣的主题，因此在 2000 年代初期引入了计算机辅助检测 (CADe) 和诊断 (CADx) 算法。尽管它们的引入寄予了很高的期望，但临床领域的潜在改进未能实现，这主要是由于每个分析图像的假阳性标记数量众多。

在过去五年中，主要由深度学习和卷积神经网络驱动的计算领域的人工智能 (AI) 革命也遍及数字乳房 X 线摄影和数字乳房断层合成中的自动乳腺癌检测领域。该领域的研究首先涉及将其能力与传统 CADe/CADx 方法的能力进行比较，这很快证明了这项新技术的潜力。在过去的几年里，已经开发出更成熟的一些商业产品，并且与经验丰富的乳腺放射科医师相比，它们的性能研究表明，这些算法在回顾性数据集中与人类的性能水平相当。尽管需要更多的研究，尤其是在真实筛查环境中进行的前瞻性评估，很明显，人工智能将在未来的乳腺癌筛查领域发挥重要作用。这个新玩家将如何塑造这个领域仍有待确定，但最近的研究已经在评估实施该技术的不同选择。

本综述的目的是概述数字乳房 X 线摄影和数字乳房断层合成中用于乳腺癌检测的 AI 领域的基本概念和发展。将讨论传统方法的缺陷，以及在大多数情况下如何通过这项新技术避免这些缺陷。重要的是，将审查评估人工智能当前能力的研究以及如何在临床领域利用这些能力的建议，同时提出在这一愿景成为现实之前需要回答的问题。