Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Skeletal Radiol. 2020 Feb;49(2):183-197. doi: 10.1007/s00256-019-03284-z. Epub 2019 Aug 4.
Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of artificial intelligence that is ideally suited to solving image-based problems. There are an increasing number of musculoskeletal applications of deep learning, which can be conceptually divided into the categories of lesion detection, classification, segmentation, and non-interpretive tasks. Numerous examples of deep learning achieving expert-level performance in specific tasks in all four categories have been demonstrated in the past few years, although comprehensive interpretation of imaging examinations has not yet been achieved. It is important for the practicing musculoskeletal radiologist to understand the current scope of deep learning as it relates to musculoskeletal radiology. Interest in deep learning from researchers, radiology leadership, and industry continues to increase, and it is likely that these developments will impact the daily practice of musculoskeletal radiology in the near future.
深度学习中的卷积神经网络（CNN）是人工智能的一个快速发展的分支，非常适合解决基于图像的问题。深度学习在肌肉骨骼系统中有越来越多的应用，可以从概念上分为病变检测、分类、分割和非解释性任务。在过去的几年中，已经有许多深度学习在所有四个类别中的特定任务中达到专家水平性能的例子，尽管还没有实现对成像检查的全面解释。对于从事肌肉骨骼放射学的医生来说，了解深度学习在肌肉骨骼放射学中的当前应用范围是很重要的。研究人员、放射科领导层和行业对深度学习的兴趣持续增加，这些发展很可能在不久的将来影响肌肉骨骼放射学的日常实践。
