Cartagena, España
Elche, España
Radiographs of the sacroiliac joints are commonly used as the first imaging methods for the diagnosis of Sacroileitis in patients with back pain. This study aims to develop and validate a new artificial intelligence approach for the automatic classification of the grade of sacroiliitis on conventional radiographs. We included a total of 267 patients with chronic back pain and clinical suggestion of Axial Spondyloarthritis who presented in a specialized center. Radiographs of sacroiliac joints were evaluated by 3 rheumatologists and 1 radiologist according to the modified New York criteria. For training the network, labeled sacroiliac joints images were resized and then several artificial neural networks were tested. Better results were achieved using the CNN-XGBoost architecture, which provided good generalizability and a high specificity with acceptable sensitivity in the detection of the grade of sacroiliitis, except for class 1. Although more studies are still needed, these artificial intelligence algorithms could potentially assist medical clinicians for the detection of radiographic sacroiliitis.
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