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Skeletal Radiol. 2020 Feb;49(2):183-197. doi: 10.1007/s00256-019-03284-z. Epub 2019 Aug 4.

Current applications and future directions of deep learning in musculoskeletal radiology.

Author information

1
Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. pauleyc@gmail.com.
2
Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

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.

KEYWORDS:

Algorithms; Applications; Artificial intelligence; Convolutional neural networks; Deep learning; Musculoskeletal; Neural networks; Radiology

PMID:
31377836
DOI:
10.1007/s00256-019-03284-z

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