Algorithmic approaches in hand imaging for rheumatic musculoskeletal diseases: A systematic literature review
Authors' conclusion
ML has significant potential to improve diagnostics and disease management in hand imaging of RMDs.
Study context
- Population
- Studies of machine learning (ML) applications in hand imaging of osteoarthritis (OA), rheumatoid arthritis (RA) and psoriatic arthritis (PsA) (PubMed, EMBASE, Scopus; search October 2024)studies included of 400 identified
- Participants
- 32 participants
- Outcome
- Characteristics of ML studies in hand imaging of rheumatic musculoskeletal diseases (RMDs)
Methodological detailsPRISMA, registered with PROSPERO, Search executed manually and with BiBot, an AI-based tool for literature reviews
- PRISMA
- Registered with PROSPERO
- Search executed manually and with BiBot, an AI-based tool for literature reviews
Main finding
As reported in the abstract
- RA most studied disease
- 88 %
- OA
- 22 %
- PsA
- 9 %
- Convolutional neural networks (most frequent algorithms)
- 50 %
- Standard radiographs (predominant imaging modality)
- 59 %
- MRI
- 16 %
- External validation conducted
- 15 %
- Datasets publicly available
- 6 %
- Interpretability tools employed
- 28 %
Original publication
Algorithmic approaches in hand imaging for rheumatic musculoskeletal diseases: A systematic literature review. Semin Arthritis Rheum. Published 2 May 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →