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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