Machine learning <i>vs</i> human experts: sacroiliitis analysis from the RAPID-axSpA and C-OPTIMISE phase 3 axSpA trials
The tested deep learning model exhibited accurate radiographic sacroiliitis detection in axSpA patients from diverse clinical trials. The proposed deep learning model could expedite diagnosis, reduce healthcare resource usage and improve patient care pathways.
Authors
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Bengt Hoepken (ORCID: https://orcid.org/0000-0002-7487-8559)
- Janis L. Vahldiek (ORCID: https://orcid.org/0000-0002-1537-1424)
- Denis Poddubnyy (ORCID: https://orcid.org/0000-0002-4537-6015)
- Fabian Proft (ORCID: https://orcid.org/0000-0003-4306-033X)
- Baran Ufuktepe (ORCID: https://orcid.org/0000-0002-8909-4028)
- Joeri Nicolaes (ORCID: https://orcid.org/0000-0003-2789-8781)
- Rachel Tham
Institutions
- Humboldt-Universität zu Berlin (DE)
- German Rheumatism Research Centre (DE)
- UCB Pharma (United Kingdom) (GB)
- Deutsches Herzzentrum München (DE)
- UCB Pharma (Germany) (DE)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
- UCB Pharma (Belgium) (BE)
- KU Leuven (BE)
Publication Details
- Journal
- Rheumatology Advances in Practice
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/rap/rkae118
- Citations
- 2
- Primary Topic
- Spondyloarthritis Studies and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 3.34
Funders
- Amgen
- Eli Lilly and Company
- Pfizer
- Gilead Sciences
- Celgene
- European Commission
- Berlin Institute of Health
- Samsung
- UCB Pharma