Abstract

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.

中文翻译：

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肌肉骨骼放射学中深度学习的当前应用和未来方向

摘要

卷积神经网络（CNN）的深度学习是人工智能的一个快速发展的子集，非常适合解决基于图像的问题。深度学习的肌肉骨骼应用越来越多，可以在概念上分为病变检测，分类，分割和非解释性任务类别。尽管尚未实现对影像学检查的全面解释，但在过去的几年中，已经展示了许多深度学习在所有四个类别的特定任务中达到专家级性能的示例。对于实践中的骨骼肌肉放射学家来说，重要的是要了解当前与骨骼肌肉放射学有关的深度学习范围。对研究人员，放射学领导，