Manubriosternal Joint Dislocation- A Treatment Dilemma

10.5580/1c3c ◽  
2011 ◽  
Vol 15 (1) ◽  
Keyword(s):  
Joint Dislocation ◽  
Manubriosternal Joint

Manubriosternal Joint Dislocation: An Unusual Risk of Trampolining

2010 ◽  
Vol 39 (5) ◽  
pp. 596-598 ◽  
Author(s):  
Iain Lyons ◽  
Sunita Saha ◽  
Thanjakumar Arulampalam
Keyword(s):  
Joint Dislocation ◽  
Manubriosternal Joint

scholarly journals Manubriosternal joint dislocation due to blunt force trauma

2019 ◽  
Vol 21 ◽  
pp. 100187 ◽  
Author(s):  
Amir A. Sarkeshik ◽  
Ala Jamal ◽  
Paul A. Perry
Keyword(s):  
Joint Dislocation ◽  
Blunt Force ◽  
Blunt Force Trauma ◽  
Manubriosternal Joint

scholarly journals Manubriosternal joint dislocation in rheumatoid arthritis: the role of thoracic kyphosis.

1986 ◽  
Vol 45 (4) ◽  
pp. 345-348 ◽  
Author(s):  
M C Kelly ◽  
N D Hopkinson ◽  
G C Zaphiropoulos
Keyword(s):  
Rheumatoid Arthritis ◽  
Thoracic Kyphosis ◽  
Joint Dislocation ◽  
Manubriosternal Joint

Delayed diagnosis of posterior sternoclavicular joint dislocation

1996 ◽  
Vol 164 (4) ◽  
pp. 242-243 ◽  
Author(s):  
George K Kiroff ◽  
David N McClure ◽  
John W Skelley
Keyword(s):  
Delayed Diagnosis ◽  
Sternoclavicular Joint ◽  
Joint Dislocation

scholarly journals Deep learning for accurately recognizing common causes of shoulder pain on radiographs

2021 ◽  
Author(s):  
Nils F. Grauhan ◽  
Stefan M. Niehues ◽  
Robert A. Gaudin ◽  
Sarah Keller ◽  
Janis L. Vahldiek ◽  
...  
Keyword(s):  
Shoulder Pain ◽  
Sensitivity And Specificity ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Clinical Situation ◽  
Assistive Device ◽  
Test Dataset ◽  
Joint Dislocation ◽  
Independent Expert ◽  
Common Causes

Abstract Objective Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. Materials and methods We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. Results The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. Conclusion CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.

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