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Abstract #4057

Deep Learning Improves Detection of Anterior Cruciate Ligament- and Meniscus Tear Detection in Knee MRI

Firas Khader1, Gustav Müller-Franzes1, Johannes Stegmaier2, Martin Pixberg3, Jonas Müller-Hübenthal3, Christiane Kuhl 1, Sven Nebelung4, and Daniel Truhn1
1Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany, 2Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany, 3Praxis im Köln Triangle, Cologne, Germany, 4Department of Diagnostic and Interventional Radiology, Düsseldorf University Hospital, Dusseldorf, Germany

In this study we aimed to analyze the capability of neural networks to accurately diagnose the presence of ACL and meniscus tears in our in-house dataset comprised of 3887 manually annotated knee MRI exams. To this end we trained the MRNet architecture on a varying number of training exams that included proton density-weighted axial, sagittal and coronal planes for each knee exam. Additionally, we compared the performance of the architecture when trained on expert vs non-expert annotations. This study demonstrates that while our neural network benefits from a larger dataset, expert annotations do not considerably improve the performance.

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