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Skeletal Radiology
Published in: Skeletal Radiology 2/2022

01-02-2022 | Osteoarthrosis | Review Article

AI MSK clinical applications: cartilage and osteoarthritis

Authors: Gabby B. Joseph, Charles E. McCulloch, Jae Ho Sohn, Valentina Pedoia, Sharmila Majumdar, Thomas M. Link

Published in: Skeletal Radiology | Issue 2/2022

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Abstract

The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.
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Metadata
Title
AI MSK clinical applications: cartilage and osteoarthritis
Authors
Gabby B. Joseph
Charles E. McCulloch
Jae Ho Sohn
Valentina Pedoia
Sharmila Majumdar
Thomas M. Link
Publication date
01-02-2022
Publisher
Springer Berlin Heidelberg
Published in
Skeletal Radiology / Issue 2/2022
Print ISSN: 0364-2348
Electronic ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-021-03909-2

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