Artificial intelligence applications for thoracic imaging
Artificial intelligence applications for thoracic imaging
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DOI：
10.1016/j.ejrad.2019.108774
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发表时间：
2020-02-01
影响因子：
3.3
通讯作者：
Revel, Marie-Pierre
中科院分区：
文献类型：
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作者：
Chassagnon, Guillaume;Vakalopoulou, Maria;Revel, Marie-Pierre
人工智能是医学成像领域的热门话题。深度学习方法的发展，特别是卷积神经网络（CNN）的使用，已经导致了经典机器学习技术的实质性性能提升。目前正在评估多种用途，特别是胸部成像，例如肺结节评估、结核或肺炎检测或弥漫性肺部疾病的定量。鉴于大量的程序和不断增加的数据可用性，胸部X射线摄影是开发用于自动解释的深度学习算法的近乎完美的领域，需要大型注释数据集。当前的算法能够检测多达14种常见的异常，当作为孤立的发现存在时。胸部计算机断层扫描是人工智能的另一个主要应用领域，特别是在大规模肺癌筛查方面。放射科医生必须理解，积极贡献并引领人工智能驱动的放射学新时代。这样的观点需要理解与机器学习相关的新术语和概念。本文的目的是提供有用的定义，了解所使用的方法和它们的可能性，并报告当前和未来的发展胸部成像。在常规临床实施之前，需要对AI工具进行前瞻性验证。
Artificial intelligence is a hot topic in medical imaging. The development of deep learning methods and in particular the use of convolutional neural networks (CNNs), have led to substantial performance gain over the classic machine learning techniques. Multiple usages are currently being evaluated, especially for thoracic imaging, such as such as lung nodule evaluation, tuberculosis or pneumonia detection or quantification of diffuse lung diseases. Chest radiography is a near perfect domain for the development of deep learning algorithms for automatic interpretation, requiring large annotated datasets, in view of the high number of procedures and increasing data availability. Current algorithms are able to detect up to 14 common anomalies, when present as isolated findings. Chest computed tomography is another major field of application for artificial intelligence, especially in the perspective of large scale lung cancer screening. It is important for radiologists to apprehend, contribute actively and lead this new era of radiology powered by artificial intelligence. Such a perspective requires understanding new terms and concepts associated with machine learning. The objective of this paper is to provide useful definitions for understanding the methods used and their possibilities, and report current and future developments for thoracic imaging. Prospective validation of AI tools will be required before reaching routine clinical implementation.