Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning
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Purpose: To evaluate diagnostic utility of two convolutional neural networks (CNNs) for severity staging anterior cruciate ligament (ACL) injuries. Materials and Methods: This retrospective analysis was conducted on 1243 knee MR images (1008 intact, 18 partially torn, 77 fully torn, 140 reconstructed ACLs) from 224 subjects collected between 2011 and 2014 (age=46.50+13.55 years, body mass index=24.58+3.60 kg/m2, 46 deviation). Images were acquired with a 3.0T MR scanner using 3D fast spin echo CUBE-sequences. The radiologists used a modified scoring metric analagous to the ACLOAS and WORMS for grading standard. To classify ACL injuries with deep learning, two types of CNNs were used, one with three-dimensional (3D) and the other with two-dimensional (2D) convolutional kernels. Performance metrics included sensitivity, specificity, weighted Cohen's kappa, and overall accuracy, followed by two-sample t-tests to compare CNN performance. Results: The overall accuracy (84 injury classification were higher using the 2D CNN than the 3D CNN. The 2D CNN and 3D CNN performed similarly in assessing intact ACLs (2D CNN: 93 sensitivity and 90 Classification of full tears by both networks were also comparable (2D CNN: 83 sensitivity and 94 The 2D CNN classified all reconstructed ACLs correctly. Conclusion: CNNs applied to ACL lesion classification results in high sensitivity and specificity, leading to potential use in helping grade ACL injuries by non-experts.
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