Published April 1, 2021 as 10.3174/ajnr.A7094 ORIGINAL RESEARCH SPINE CT Cervical Spine Fracture Detection Using a Convolutional Neural Network J.E. Small, P. Osler, A.B. Paul, and M. Kunst ABSTRACT BACKGROUND AND PURPOSE: Multidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. Our aim was to evaluate the performance of a convolutional neural network in the detection of cervical spine fractures on CT. MATERIALS AND METHODS: We evaluated C-spine, an FDA-approved convolutional neural network developed by Aidoc to detect cer- vical spine fractures on CT. A total of 665 examinations were included in our analysis. Ground truth was established by retrospective vis- ualization of a fracture on CT by using all available CT, MR imaging, and convolutional neural network output information. The Œ coefﬁcients, sensitivity, speciﬁcity, and positive and negative predictive values were calculated with 95% CIs comparing diagnostic accu- racy and agreement of the convolutional neural network and radiologist ratings, respectively, compared with ground truth. RESULTS: Convolutional neural network accuracy in cervical spine fracture detection was 92% (95% CI, 90%–94%), with 76% (95% CI, 68%–83%) sensitivity and 97% (95% CI, 95%–98%) speciﬁcity. The radiologist accuracy was 95% (95% CI, 94%–97%), with 93%
American Journal of Neuroradiology – American Journal of Neuroradiology
Published: Apr 1, 2021
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