Keywords: Diagnosis/Prediction, Infection, spondylitis
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords