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Skeletal Radiology
Erschienen in: Skeletal Radiology 2/2020

04.08.2019 | Review Article

Current applications and future directions of deep learning in musculoskeletal radiology

verfasst von: Pauley Chea, Jacob C. Mandell

Erschienen in: Skeletal Radiology | Ausgabe 2/2020

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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.
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Metadaten
Titel
Current applications and future directions of deep learning in musculoskeletal radiology
verfasst von
Pauley Chea
Jacob C. Mandell
Publikationsdatum
04.08.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Skeletal Radiology / Ausgabe 2/2020
Print ISSN: 0364-2348
Elektronische ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-019-03284-z

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