Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance.

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Title: Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance.
Authors: Vuskov R; Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. rvuskov@ukaachen.de.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. rvuskov@ukaachen.de., Hermans A; Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Visual Computing Institute (Computer Vision), RWTH Aachen University, Aachen, Germany., Pixberg M; Radiologic Practice Cologne Triangle, Cologne, Germany., Müller-Hübenthal J; Radiologic Practice Cologne Triangle, Cologne, Germany., Brauksiepe A; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Corban E; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Cubukcu M; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Nowak J; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Kargaliev A; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., von der Stück M; Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Siepmann R; Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Kuhl C; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Truhn D; Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Nebelung S; Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
Source: European radiology [Eur Radiol] 2026 Apr; Vol. 36 (4), pp. 2563-2575. Date of Electronic Publication: 2025 Oct 17.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1432-1084
DOI:10.1007/s00330-025-12052-8