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

Nikan K. Namiri; Io Flament; Bruno Astuto; Rutwik Shah; Radhika Tibrewala; Francesco Caliva; Thomas M. Link; Valentina Pedoia; Sharmila Majumdar

arXiv · 2003.09089

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

Nikan K. Namiri·Io Flament·Bruno Astuto·Rutwik Shah·Radhika Tibrewala·Francesco Caliva·Thomas M. Link·Valentina Pedoia·Sharmila Majumdar

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Abstract

Purpose: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of 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, and 140 reconstructed ACLs) from 224 patients (age 47 +/- 14 years, 54% women) acquired between 2011 and 2014. The radiologists used a modified scoring metric. 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 McNemar's test to compare the CNNs performance. Results: The overall accuracy and weighted Cohen's kappa reported for ACL injury classification were higher using the 2D CNN (accuracy: 92% (233/254) and kappa: 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 (2D CNN: 93% (188/203) sensitivity and 90% (46/51) specificity; 3D CNN: 89% (180/203) sensitivity and 88% (45/51) specificity). Classification of full tears by both networks were also comparable (2D CNN: 82% (14/17) sensitivity and 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 high sensitivity and specificity, suggesting that these networks could be used to help grade ACL injuries by non-experts.

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Nikan K. Namiri, Io Flament, Bruno Astuto, Rutwik Shah, Radhika Tibrewala, Francesco Caliva, Thomas M. Link, Valentina Pedoia, Sharmila Majumdar. 2020-04-13. Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images. https://arxiv.org/abs/2003.09089

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