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Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Bressem, Keno K; Vahldiek, Janis L; Adams, Lisa; Niehues, Stefan Markus; Haibel, Hildrun; Rodriguez, Valeria Rios; Torgutalp, Murat; Protopopov, Mikhail; Proft, Fabian; Rademacher, Judith; Sieper, Joachim; Rudwaleit, Martin; Hamm, Bernd; Makowski, Marcus R; Hermann, Kay-Geert; Poddubnyy, Denis
Title:
Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance.
Abstract:
BACKGROUND: Radiographs of the sacroiliac joints are commonly used for the diagnosis and classification of axial spondyloarthritis. The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spondyloarthritis (axSpA). METHODS: Conventional radiographs of the sacroiliac joints obtained in two independent studies of patients with axSpA were used. The first cohort comprised 1553 radiographs and w...     »
Journal title abbreviation:
Arthritis Res Ther
Year:
2021
Journal volume:
23
Journal issue:
1
Fulltext / DOI:
doi:10.1186/s13075-021-02484-0
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/33832519
Print-ISSN:
1478-6354
TUM Institution:
Institut für Diagnostische und Interventionelle Radiologie
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