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Abstract #0200

Can A Machine Diagnose Knee MR Images? Fully-automated Cartilage Lesion Detection by using Deep Learning

Fang Liu1, Zhaoye Zhou2, Kevin Lian1, Shivhumar Kambhampati1, and Richard Kijowski1

1Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 2Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States

This study evaluated a fully-automated cartilage lesion detection system utilizing a deep convolutional neural network (CNN) to segment bone and cartilage followed by a second CNN classification network to detect structural abnormalities within the segmented tissues. The CNN network was trained to detect cartilage lesions within the knee joint using sagittal fat-suppressed T2-weighted fast spin-echo images in 125 subjects. The proposed CNN model achieved high diagnostic accuracy for detecting cartilage lesions with a 0.914 area under curve on receiver operation characteristics analysis. The optimal threshold for sensitivity and specificity of the CNN model was 84.3% and 84.6% respectively.

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