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Abstract #1406

Meniscal Tear Detection with Machine Learning: Initial Experience

Eric M Bultman1, Akshay S Chaudhari1, Arjun D Desai1, and Garry E Gold1

1Radiology, Stanford University, Stanford, CA, United States

Despite rapid recent advances in convolutional neural networks used for image classification, generalizability of these networks to medical image data has not been thoroughly investigated. In this work, we utilize two networks designed to classify ImageNet natural-image data – Inception-v3 and ResNet-50 – and investigate their performance in classifying meniscal tears on MR examinations of the knee. Using limited segmentation and manual tear identification, slice-wise sensitivity of 0.68 and 0.58 is achieved for the respective networks. Applying the “two-slice-touch” rule, sensitivity is significantly increased, but with concomitant decrease in specificity. Our results support the feasibility of utilizing CNNs for meniscal tear identification.

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