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64%, respectively, using a sensitivity driven variant. By comparison, accuracy, sensitivity, and specificity achieved by board-certified veterinary radiologists was 82.71%, 68.42%, and 87.09%, respectively. Although overall accuracy of the accuracy driven convolutional neural network algorithm and veterinary radiologists was identical, concordance between the two approaches was 85.19%. #link# This study documents proof-of-concept for application of deep learning techniques for computer-aided diagnosis in veterinary medicine.Witteveen-Kolk
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