Detecting radiographic sacroiliitis using deep learning with expert-level accuracy in axial spondyloarthritis

ABSTRACT Conventional radiographs of sacroiliac joints from two independent cohorts of patients with axial spondyloarthritis (axSpA) were used to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of the disease. The first cohort consisting of 1669 radiographs labelled by three experts was used for training. From the second cohort 100 radiographs labelled by two experts were randomly selected for the test dataset. The neural network achieved an excellent performance in detection of definite radiographic sacroiliitis with an area under the receiver operating characteristics curve of 0.97 and 0.96 for the validation and test datasets, respectively. Sensitivity and specificity for the cut-off weighting both measurements equally were 0.90 and 0.93 for the validation and 0.87 and 0.97 for the test set. The Cohen’s kappa between the neural network and the reference judgements were 0.80 for both validation and test sets, and the absolute agreement on the classification yielded 91% and 90%, respectively.