University of Oulu

M.T. Nieminen, V. Casula, M.T. Nevalainen, S. Saarakkala, Osteoarthritis year in review 2018: imaging, Osteoarthritis and Cartilage, Volume 27, Issue 3, 2019, Pages 401-411, ISSN 1063-4584, https://doi.org/10.1016/j.joca.2018.12.009

Osteoarthritis year in review 2018 : imaging

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Author: Nieminen, M. T.1,2; Casula, V.1; Nevalainen, M. T.1,2;
Organizations: 1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019121648350
Language: English
Published: Elsevier, 2019
Publish Date: 2019-12-25
Description:

Summary

Purpose: To provide a narrative review of the most relevant original research published in 2017/2018 on osteoarthritis imaging.

Methods: The PubMed database was used to recover all relevant articles pertaining to osteoarthritis and medical imaging published between 1 April 2017 and 31 March 2018. Review articles, case studies and in vitro or animal studies were excluded. The original publications were subjectively sorted based on relevance, novelty and impact.

Results and conclusions: The publication search yielded 1,155 references. In the assessed publications, the most common imaging modalities were radiography (N = 708) and magnetic resonance imaging (MRI) (355), followed by computed tomography (CT) (220), ultrasound (85) and nuclear medicine (17). An overview of the most important publications to the osteoarthritis (OA) research community is presented in this narrative review. Imaging studies play an increasingly important role in OA research, and have helped us to understand better the pathophysiology of OA. Radiography and MRI continue to be the most applied imaging modalities, while quantitative MRI methods and texture analysis are becoming more popular. The value of ultrasound in OA research has been demonstrated. Several multi-modality predictive models have been developed. Deep learning has potential for more automatic and standardized analyses in future OA imaging research.

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Series: Osteoarthritis and cartilage
ISSN: 1063-4584
ISSN-E: 1522-9653
ISSN-L: 1063-4584
Volume: 27
Issue: 3
Pages: 401 - 411
DOI: 10.1016/j.joca.2018.12.009
OADOI: https://oadoi.org/10.1016/j.joca.2018.12.009
Type of Publication: A2 Review article in a scientific journal
Field of Science: 114 Physical sciences
217 Medical engineering
3111 Biomedicine
3121 General medicine, internal medicine and other clinical medicine
Subjects:
Funding: The authors are grateful for financial support from Jane and Aatos Erkko Foundation, Academy of Finland (project 268378), Northern Ostrobothnia Hospital District and University of Oulu. The salaries of the authors are comprised from contributions from the abovementioned bodies. The bodies had no involvement in the study design, collection, analysis or interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.
Academy of Finland Grant Number: 268378
Detailed Information: 268378 (Academy of Finland Funding decision)
Copyright information: © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
  https://creativecommons.org/licenses/by-nc-nd/4.0/