Osteoarthritis year in review 2020: imaging

Eckstein, F.; Wirth, W.; Culvenor, Adam G

doi:10.1016/j.joca.2020.12.019

Cited by 18 publications

(7 citation statements)

References 98 publications

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“…There are limited trials that objectively investigate structural changes in those completing physiotherapy programs. In this field, there has not been significant research conducted in recent years regarding hip joint imaging [ 28 , 29 , 30 ] or the long-term effects of physiotherapeutic treatment [ 31 , 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

The Use of Scoring Hip Osteoarthritis with MRI as an Assessment Tool for Physiotherapeutic Treatment in Patients with Osteoarthritis of the Hip

Lewińska

Palczewski

Piłat

et al. 2021

JCM

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Rehabilitation programs are considered effective at reducing the impact of osteoarthritis (OA) of the hip; however, studies using reliable measures related to OA biomarkers to assess the effects of rehabilitation are lacking. The objective of this study was to investigate whether an MRI-based (Magnetic Resonance Imaging-based), semi-quantitative system for an OA severity assessment is feasible for the evaluation of the structural changes in the joint observed during a long-term physiotherapy program in patients with hip OA. The study group consisted of 37 adult OA patients who participated in a 12-month physiotherapy program. The Scoring hip osteoarthritis with MRI (SHOMRI) system was used to evaluate the severity of structural changes related to hip OA. Hip disability and the osteoarthritis outcome score (HOOS) and the core set of performance-based tests recommended by Osteoarthritis Research Society International were used for functional assessment. SHOMRI showed excellent inter- and intra-rater agreement, proving to be a reliable method for the evaluation of hip abnormalities. At the 12-month follow-up no statistically significant changes were observed within the hip joint; however, a trend of structural progression was detected. There was a negative correlation between most of the SHOMRI and HOOS subscales at baseline and the 12-month follow-up. Although SHOMRI provides a reliable assessment of the hip joint in patients with OA it showed a limited value in detecting significant changes over time in the patients receiving physiotherapy over a 12-month period.

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Section: Discussionmentioning

confidence: 99%

The Use of Scoring Hip Osteoarthritis with MRI as an Assessment Tool for Physiotherapeutic Treatment in Patients with Osteoarthritis of the Hip

Lewińska

Palczewski

Piłat

et al. 2021

JCM

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“…It is also possible that rather than looking to new or improved imaging modalities, the current ones may already be able to detect the earlier changes in OA, but we simply do not know what those subtle changes or combination of changes are. This prospect could be realized through the application of machine learning and deep learning, which have already been shown to be able to aid imaging segmentation, disease diagnosis and image reconstruction (127)(128)(129). Both machine learning and deep learning are branches of artificial intelligence (AI), which have expanded exponentially over the past decade.…”

Section: A Second Look?mentioning

confidence: 99%

Moving Beyond the Limits of Detection: The Past, the Present, and the Future of Diagnostic Imaging in Canine Osteoarthritis

2022

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Osteoarthritis (OA) is the most common orthopedic condition in dogs, characterized as the chronic, painful end-point of a synovial joint with limited therapeutic options other than palliative pain control or surgical salvage. Since the 1970s, radiography has been the standard-of-care for the imaging diagnosis of OA, despite its known limitations. As newer technologies have been developed, the limits of detection have lowered, allowing for the identification of earlier stages of OA. Identification of OA at a stage where it is potentially reversible still remains elusive, however, yet there is hope that newer technologies may be able to close this gap. In this article, we review the changes in the imaging of canine OA over the past 50 years and give a speculative view on future innovations which may provide for earlier identification, with the ultimate goal of repositioning the limit of detection to cross the threshold of this potentially reversible disease.

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“…The segmentation and subsequent quantitative evaluation of interested organs in medical images provide valuable information for pathological analysis. It is important for the planning of treatment strategy, the monitoring of disease progress, and the prediction of patient prognosis [ 1 , 2 , 3 ]. When doctors make a diagnosis, they usually first manually segment the organizations of interest in medical images and then perform quantitative and qualitative evaluations [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation

Chen

Qiu

Yang

et al. 2022

Sensors

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The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods do not comprehensively explore multiscale feature fusion, and there is still much room for improvement. In this paper, we propose a novel multiresolution aggregation transformer UNet (MRA-TUNet) based on multiscale input and coordinate attention for medical image segmentation. It realizes multiresolution aggregation from the following two aspects: (1) On the input side, a multiresolution aggregation module is used to fuse the input image information of different resolutions, which enhances the input features of the network. (2) On the output side, an output feature selection module is used to fuse the output information of different scales to better extract coarse-grained information and fine-grained information. We try to introduce a coordinate attention structure for the first time to further improve the segmentation performance. We compare with state-of-the-art medical image segmentation methods on the automated cardiac diagnosis challenge and the 2018 atrial segmentation challenge. Our method achieved average dice score of 0.911 for right ventricle (RV), 0.890 for myocardium (Myo), 0.961 for left ventricle (LV), and 0.923 for left atrium (LA). The experimental results on two datasets show that our method outperforms eight state-of-the-art medical image segmentation methods in dice score, precision, and recall.

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Osteoarthritis year in review 2020: imaging

Cited by 18 publications

References 98 publications

The Use of Scoring Hip Osteoarthritis with MRI as an Assessment Tool for Physiotherapeutic Treatment in Patients with Osteoarthritis of the Hip

The Use of Scoring Hip Osteoarthritis with MRI as an Assessment Tool for Physiotherapeutic Treatment in Patients with Osteoarthritis of the Hip

Moving Beyond the Limits of Detection: The Past, the Present, and the Future of Diagnostic Imaging in Canine Osteoarthritis

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation

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