Differentiation Magnetic Resonance Images of Tuberculous and Brucellar Spondylitis Using Convolutional Neural Network Based on VGG19

Motivation: Tuberculous spondylitis （TS） and brucellosis spondylitis （BS） are two common infectious diseases in spinal surgery, and the differential diagnosis of these diseases is challenging but important to ensure appropriate treatment. Goal(s): The aim of this study was to evaluate the performance of a convolutional neural network CNN) based on VGG19 in distinguishing between TS and BS on different parameter magnetic resonance imaging （MRI） and to compare it with three radiologists. Approach: MRIs of 383 patients were randomly divided into training （n = 307） and validation （n = 76） groups. Results: VGG19-based CNN outperforms radiologist assessment in distinguishing TS from BS. Impact: The proposed CNN based on VGG19 is effective in diagnosing TS and BS on MRI, which could not only help in clinical decision making, but also improve efficiency and reduce medical costs.