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Purpose: To evaluate the diagnostic utility of two convolutional neuralnetworks (CNNs) for severity staging of anterior cruciate ligament (ACL)injuries.  Materials and Methods: This retrospective analysis was conducted on 1243 kneeMR images (1008 intact, 18 partially torn, 77 fully torn, and 140 reconstructedACLs) from 224 patients (age 47 +/- 14 years, 54% women) acquired between 2011and 2014. The radiologists used a modified scoring metric. To classify ACLinjuries with deep learning, two types of CNNs were used, one withthree-dimensional (3D) and the other with two-dimensional (2D) convolutionalkernels. Performance metrics included sensitivity, specificity, weightedCohen's kappa, and overall accuracy, followed by McNemar's test to compare theCNNs performance.  Results: The overall accuracy and weighted Cohen's kappa reported for ACLinjury classification were higher using the 2D CNN (accuracy: 92% (233/254) andkappa: 0.83) than the 3D CNN (accuracy: 89% (225/254) and kappa: 0.83) (P =.27). The 2D CNN and 3D CNN performed similarly in classifying intact ACLs (2DCNN: 93% (188/203) sensitivity and 90% (46/51) specificity; 3D CNN: 89%(180/203) sensitivity and 88% (45/51) specificity). Classification of fulltears by both networks were also comparable (2D CNN: 82% (14/17) sensitivityand 94% (222/237) specificity; 3D CNN: 76% (13/17) sensitivity and 100%(236/237) specificity). The 2D CNN classified all reconstructed ACLs correctly.  Conclusion: 2D and 3D CNNs applied to ACL lesion classification had highsensitivity and specificity, suggesting that these networks could be used tohelp grade ACL injuries by non-experts.

Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images