A dataset containing 1257 shoulder radiographs from 406 patients. Shoulder fractures in the humerus, clavicle and scapula are classified according to the 2018 AO/OTA classification. The radiographs are also classified for degenerative conditions such as osteoarthritis, avascular necrosis and cuff arthropathy. The radiographs are classified by experienced orthopaedic surgeons specialized in shoulder surgery.

Keywords: Pathology, Annotated, Plain radiographs, Shoulder, Clavicle, Scapula, Humerus, Fracture, Osteoarthritis, Cuff arthropathy.

Sample images

Sample images with reduced image quality. Please click to preview.

Dataset information

Short name SR2023
Origin Clinical
Cite as Max Gordon and Martin Magnéli (2023) Shoulder radiographs classified on both fractures according to 2018 AO/OTA and classified degenerative conditions doi:10.23698/aida/sr2023
[BibTeX format]
Field Radiology
Organ Shoulder
Age span 15+
Title Shoulder radiographs classified on both fractures according to 2018 AO/OTA and classified degenerative conditions
Author Max Gordon
Martin Magnéli
Year 2023
DOI doi:10.23698/aida/sr2023
Status Completed
Version 1.0.0
Scans 1257
Annotations 166
Size 2.7GB
Resolution Various. Image sizes vary from 800x600px to 2000x1000px.
Modality CR
Scanner
Stain
Phase
References
  1. Magnéli M, Ling P, Gislén J, Fagrell J, Demir Y, Arverud ED, et al. (2023) Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle. PLoS ONE 18(8): e0289808. https://doi.org/10.1371/journal.pone.0289808
Copyright Copyright 2023 Karolinska Institutet, Max Gordon
Access

Available under the following licenses, described in the License section below.

Controlled access
Free for use in legal and ethical medical diagnostics research.

AIDA BY license
Free for use within AIDA with attribution.

Annotation

Manually annotated by experienced orthopedic surgeons. Classification for fractures according to 2018 AO/OTA. OA according to Samilson-Prieto. Avascular necrosis according to Cruess. Cuff arthropathy according to Hamada. Glenoid according to Favard.

License

Controlled access

Free for use in legal and ethical medical diagnostics research.

To request access to the dataset, use the Apply for access button below. Note that the recipient researcher must hold at least a PhD degree in a relevant field and that the applicant should be an authorized signatory who can legally enter into data sharing agreements on the behalf of the institution.

Apply for access

Help with applying for controlled access.

AIDA BY license

Copyright 2023 Karolinska Institutet, Max Gordon

Permission to use, copy, modify, and/or distribute this data within Analytic Imaging Diagnostics Arena (AIDA) for the purpose of medical diagnostics research with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies, and that publications resulting from the use of this data cite the following works:

Max Gordon and Martin Magnéli (2023) Shoulder radiographs classified on both fractures according to 2018 AO/OTA and classified degenerative conditions doi:10.23698/aida/sr2023.

Magnéli M, Ling P, Gislén J, Fagrell J, Demir Y, Arverud ED, et al. (2023) Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle. PLoS ONE 18(8): e0289808. https://doi.org/10.1371/journal.pone.0289808

THE DATA IS PROVIDED “AS IS” AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS DATA INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR CHARACTERISTICS OF THIS DATA.