Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance.

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作者： Keno K,Bressem ^[1] ; Janis L,Vahldiek ^[2] ; Lisa,Adams ^[3] ; Stefan Markus,Niehues ^[1] ; Hildrun,Haibel ^[2] ; Valeria Rios,Rodriguez ^[1] ; Murat,Torgutalp ^[4] ; Mikhail,Protopopov ^[4] ; Fabian,Proft ^[4] ; Judith,Rademacher ^[4] ; Joachim,Sieper ^[4] ; Martin,Rudwaleit ^[2] ; Bernd,Hamm ^[4] ; Marcus R,Makowski ^[4] ; Kay-Geert,Hermann ^[5] ; Denis,Poddubnyy ^[1]

作者单位： Department of Radiology, Charité - Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany. ^[1] Berlin Institute of Health, BIH, Berlin, Germany. ^[2] Department of Radiology, Charité - Universitätsmedizin Berlin, Hindenburgdamm 30, 12203, Berlin, Germany. janis.vahldiek@charite.de. ^[3] Department of Gastroenterology, Infectious Diseases and Rheumatology, Charité - Universitätsmedizin Berlin, Berlin, Germany. ^[4] Department of Internal Medicine and Rheumatology, Klinikum Bielefeld Rosenhöhe, Bielefeld, Germany. ^[5] Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany. ^[6] Department of Epidemiology, German Rheumatism Research Centre, Berlin, Germany. ^[7]

关键词 Artificial intelligence Axial spondyloarthritis Deep learning Machine learning Sacroiliitis

主题词人类(Humans);磁共振成像(Magnetic Resonance Imaging);放射摄影术(Radiography);骶髂关节(Sacroiliac Joint);骶髂关节炎(Sacroiliitis);椎关节炎(Spondylarthritis)

DOI 10.1186/s13075-021-02484-0

PMID 33832519

发布时间 2024-03-31

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