Automated detection and classification of the proximal humerus fracture by using deep learning algor...
Automated detection and classification of the proximal humerus fracture by using deep learning algorithm
About this item
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Author / Creator
Publisher
England: Taylor & Francis
Journal title
Language
English
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Publication information
Publisher
England: Taylor & Francis
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Scope and Contents
Contents
Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs.
Patients and methods - 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater...
Alternative Titles
Full title
Automated detection and classification of the proximal humerus fracture by using deep learning algorithm
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Record Identifier
TN_cdi_proquest_miscellaneous_2018668638
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2018668638
Other Identifiers
ISSN
1745-3674
E-ISSN
1745-3682
DOI
10.1080/17453674.2018.1453714
