OP0254 AN ARTIFICIAL NEURAL NETWORK FOR THE DETECTION OF DEFINITE RADIOGRAPHIC SACROILIITIS WITH HIGH SPECIFICITY IN THE DIAGNOSTIC SETTING
Authors: Vahldiek J. L. , Bressem K. K. , Niehues S. , Adams L. , Spiller L. , Protopopov M. , Rios Rodriguez V. , Muche B. , Rademacher J. , Haibel H. , Torgutalp M. , Proft F. , Poddubnyy D. 
Published: May 2021

Background:

Radiographs of the sacroiliac joints are commonly used as the first imaging method for the diagnosis of axial spondyloarthritis (axSpA), but the reliability of sacroiliitis detection is usually low. Recently we developed a deep convolutional neural network (CNN) that can detect radiographic sacroiliitis with expert-like accuracy in patients with axSpA, i.e., classification as radiographic or non-radiographic [1]. There is frequent criticism that many artificial intelligence algorithms for diagnostic analysis of medical images lack robust validation in real-world clinical applications.

Objectives:

The aim of this study was to evaluate the performance of our deep CNN in detecting definite radiographic sacroiliitis in the diagnostic setting.

Methods:

In this study, we included a total of 361 patients with chronic back pain who presented to a rheumatologist in a specialized SpA center with a suspicion of axSpA within the OptiRef project [2]. All patients received a structured rheumatologic diagnostic work-up that resulted in the final diagnosis of axial SpA/no axial SpA. Radiographs of sacroiliac joints were evaluated by a rheumatologist and radiologist according to the modified New York criteria; the consensus judgement of the presence of definite radiographic sacroiliitis (>=grade 2 bilaterally or >=grade 3 unilaterally) was used as a reference. The predictions of the deep CNN’s inference (with a balanced cutoff of 0.724 for the predictions of the model that was derived from the training and validation steps [1]) on all available pelvic radiographs was compared to this reference judgement.

Results:

Pelvic radiographs of 340 patients (110 with axSpA including 61 patient with radiographic and 49 with non-radiographic axSpA, and 230 without SpA) were available for the CNN evaluation. The deep CNN achieved a sensitivity of 79% for the diagnosis of radiographic axSpA. The specificity of radiographic sacroiliitis detection was 94% (Table 1). The area under the receiver operating characteristics curve for the prediction of the presence of definite radiographic sacroiliitis was 88%. Figure 1 shows an exemplary class activation map of our CNN.

Table 1.

Convolutional neural network predictions of the presence of radiographic sacroiliitis in patients with suspected axSpA according to the final diagnosis by rheumatologist in OptiRef (N=340).

Clinical diagnosisCNN’s prediction on the presence of definite radiographic sacroiliitisPresentNot presentRadiographic axial SpA48/61 (78.7%)13/61 (21.3%)Non-radiographic axial SpA4/49 (8.2%)45/49 (91.8%)No SpA (other diagnosis)14/230 (6.1%)216/230 (93.9%)

Conclusion:

The artificial neural network showed good generalizability and a high specificity with acceptable sensitivity in the detection of radiographic sacroiliitis when applied in the diagnostic setting of patients with chronic back pain and suspicion of axSpA. This algorithm can therefore be used to aid the detection of radiographic sacroiliitis as a part of the diagnostic approach.

References:

[1]Bressem KK, et al. medRxiv. 2020:2020.05.19.20105304.

[2]Proft F, et al. Semin Arthritis Rheum. 2020;50:1015-1021.

Acknowledgements:

The OptiRef study was supported by a research grant from Novartis.

Disclosure of Interests:

None declared