Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
Keno K. Bressem(TUM Klinikum), Denis Poddubnyy(German Rheumatism Research Centre), Martín Rudwaleit(Klinikum Bielefeld), Valeria Ríos Rodríguez(Charité - Universitätsmedizin Berlin), Stefan M. Niehues(Charité - Universitätsmedizin Berlin), Hildrun Haibel(Charité - Universitätsmedizin Berlin), Janis L. Vahldiek(Charité - Universitätsmedizin Berlin), Mikhail Protopopov(Charité - Universitätsmedizin Berlin), Fabian Proft(Charité - Universitätsmedizin Berlin), Marcus R. Makowski(TUM Klinikum), Murat Torğutalp(Charité - Universitätsmedizin Berlin), Joachim Sieper(Freie Universität Berlin), Kay‐Geert Hermann(Charité - Universitätsmedizin Berlin), Lisa C. Adams(Palo Alto University), Judith Rademacher(Berlin Institute of Health at Charité - Universitätsmedizin Berlin), Bernd Hamm(Charité - Universitätsmedizin Berlin)
Cited by 89
Related Papers
Prevention and treatment of low back pain: evidence, challenges, and promising directions
|The Lancet|2018|2.4k
Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021
|The Lancet|2025|801
Ankylosing spondylitis: an overview
|Annals of the Rheumatic Diseases|2002|659
Evaluation and mitigation of the limitations of large language models in clinical decision-making
|Nature Medicine|2024|555
End-to-end privacy preserving deep learning on multi-institutional medical imaging
|Nature Machine Intelligence|2021|468