Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance
Question Increasing MRI utilization adds pressure on radiologists, necessitating comprehensive AI models for image analysis to manage this growing demand efficiently. Findings Our AI model enhanced diagnostic performance and efficiency of resident radiologists when reading knee MRI studies, demonstrating robust results across diverse conditions and two datasets. Clinical relevance Model assistance increases the sensitivity of radiologists, helping to identify pathologies that were overlooked without AI assistance. Reduced reading times suggest potential alleviation of radiologists' workload.
Authors
- Daniel Truhn (ORCID: https://orcid.org/0000-0002-9605-0728)
- J. A. Nowak
- Robert Siepmann (ORCID: https://orcid.org/0000-0002-4608-5180)
- Eric Corban
- Christiane Katharina Kuhl (ORCID: https://orcid.org/0000-0001-8696-2363)
- Jonas Müller-Hübenthal
- Alexander Hermans (ORCID: https://orcid.org/0000-0003-2127-0782)
- Marc Huppertz (ORCID: https://orcid.org/0009-0004-2801-3679)
- Sven Nebelung (ORCID: https://orcid.org/0000-0002-5267-9962)
- Roman Vuskov (ORCID: https://orcid.org/0009-0006-5022-9120)
- M Pixberg
- Andreas Brauksiepe
- Malin Cubukcu
- Aleksandar Kargaliev
Institutions
- University Hospital Cologne (DE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- European Radiology
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1007/s00330-025-12052-8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Deutsche Forschungsgemeinschaft
- RWTH Aachen University