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Abstract #0243

Semi-Quantitative Grading of the Anterior Cruciate Ligament using Deep Learning

Nikan K Namiri1, Io Flament1, Bruno Astuto1, Rutwik Shah1, Radhika Tibrewala1, Francesco Caliva1, Thomas M Link1, Valentina Pedoia1, and Sharmila Majumdar1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States

In this study we present a fully-automated anterior cruciate ligament (ACL) detection and classification framework which provides multi-class severity staging of ACL tears using state-of-the-art deep learning architectures. We compared the performances of a 3D and a 2D convolutional neural network (CNN) in ACL lesion classification. A higher overall accuracy (84%) and linear-weighted kappa (.92) were observed with the 2D model; however, it underperformed compared to the 3D CNN in classifying partial tears. This is the first reported deep learning detection and classification pipeline for ACL severity staging, including reconstructed, fully torn, partially torn, and intact ligaments.

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