OP0145 DIFFERENTIAL DIAGNOSIS OF RA AND PSA USING NEURAL NETWORKS ON THREE-DIMENSIONAL BONE SHAPE OF FINGER JOINTS
Authors: Folle L. , Liu C. , Simon D. , Meinderink T. , Liphardt A. M. , Krönke G. , Schett G. , Maier A. , Kleyer A. 
Published: May 2021

Background:Early diagnosis and reliable differentiation between rheumatic diseases (RMDs) are crucial to start an adequate therapy and prevent irreversible damage. Since finger joints are commonly affected in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), imaging of the peripheral skeleton is an essential step of diagnosis at a rheumatologist. High resolution peripheral quantitative computed tomography (HR-pQCT) allows an even more detailed and three-dimensional (3D) illustration of the peripheral bone than conventional radiographs. Segmented scans contain further information, such as the density, microstructure, and shape of the bones, which can be further analyzed by neural networks.Objectives:We hypothesize that, based on the shape of the second metacarpophalangeal (MCP) joint from HR-pQCT images, a neural network can be trained to differentiate between RA, PsA, and healthy controls and to reveal regions in the bone shape characteristic for the diseases.Methods:HR-pQCT images of MCP joints from patients with classified CCP positive RA, classified PsA, and healthy controls with low motion artifacts and appropriate scan region were selected as reported previously [3]. Scans were performed as part of the clinical routine and patients gave their informed consent to use pseudonymized data (Ethics approval 334_16B). Based on the assumption that pathognomonic changes develop over time, only images were used, where the period between classification and imaging exceeded one year.Based on previous work [4], a pixel-wise mask of the second metacarpal bone was generated using a neural network based on the HR-pQCT scans of patients. Supervised auto-encoder [1] networks were used to predict the correct class given the bone mask only. For the neural network experiment, the patient scans were split on a patient-level into training (70%), validation (20%), and testing (10%). Guided backpropagation [2] was used as a method to investigate the regions influencing the class prediction most.Results:In total, images of 331 patients were included in the experiments. The evaluation of the model on the 33 test cases yielded a high accuracy for the healthy control with 94%, RA patients with 84%, and PsA patients with 89%. An area under the receiver operator curve of 91% could be achieved. The regions of the bone mask influencing the network´s decision most are highlighted exemplary in Figure 1.Figure 1.Visualization of the HR-pQCT slices with gradient maps. Higher values (red) represent regions that had a stronger contribution to the classification result. The HR-pQCT images are displayed for reference only. (a) Healthy patient, (b) RA diagnosed patient, and (c) PsA diagnosed patient. The first row shows the single slices with the highest values corresponding to the 3D bone masks in the second row.Conclusion:For the first time, a neural network-based approach successfully provides a differential diagnosis of RA and PsA based only on the shape of the second MCP in HR-pQCT images. The evaluation of the test set suggests that high curvatures of the bone surface in the joint region significantly influence the prediction of the network, suggesting an in-depth investigation of these regions for patients affected by RA and PsA. Based on these promising findings, we aim to extend the approach to seronegative RA as well as early RA and PsA.References:[1]Le, L. et al. (2018). Supervised autoencoders: Improving generalization performance with unsupervised regularizers. In Advances in Neural Information Processing Systems.[2]Springenberg, J. T. et al. (2015). Striving for simplicity: The all convolutional net. 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings.[3]Simon, D. et al. (2017). Age- and Sex-Dependent Changes of Intra-articular Cortical and Trabecular Bone Structure and the Effects of Rheumatoid Arthritis. Journal of Bone and Mineral Research, 32(4), 722–730.[4]Folle, L. et al. (2021). Fully Automatic Bone Mineral Density Measurements using Deep Learning. Manuscript submitted for publication.Acknowledgements:This work was supported by the emerging field initiative (project 4 Med 05 “MIRACLE”) of the University Erlangen-Nürnberg and MASCARA - Molecular Assessment of Signatures Characterizing the Remission of Arthritis grant 01EC1903A.Disclosure of Interests:Lukas Folle: None declared, Chang Liu: None declared, David Simon Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Lilly, Novartis, Timo Meinderink: None declared, Anna-Maria Liphardt Consultant of: Mylan/Meda Pharma, Grant/research support from: Novartis, Gerhard Krönke Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Lilly, Novartis, Georg Schett Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Lilly, Novartis, Andreas Maier: None declared, Arnd Kleyer Speakers bureau: Lilly, Novartis, Consultant of: Lilly, Novartis, Gilead, BMS, Abbvie, Grant/research support from: Novartis, Lilly