Skip to main content

Advertisement

SpringerLink
Log in

Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography

  • Scientific Article
  • Published:
Skeletal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance.

Materials and methods

A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients. Binary discriminatory capacity (no arthritis versus arthritis; RA versus not RA) and three-way classification (no arthritis versus OA versus RA) were evaluated. The effects of additional pretraining using musculoskeletal radiographs, using all views as opposed to only the posteroanterior view, and varying image resolution on model performance were also investigated. Area under the receiver operating characteristic curve (AUC) and Cohen’s kappa coefficient were used to evaluate diagnostic performance.

Results

For no arthritis versus arthritis, the model achieved an AUC of 0.975 (95% CI: 0.957, 0.989). For RA versus not RA, the model achieved an AUC of 0.955 (95% CI: 0.919, 0.983). For three-way classification, the model achieved a kappa of 0.806 (95% CI: 0.742, 0.866) and accuracy of 87.2% (95% CI: 83.2%, 91.2%) on the test set. Increasing image resolution increased performance up to 1024 × 1024 pixels. Additional pretraining on musculoskeletal radiographs and using all views did not significantly affect performance.

Conclusion

A deep learning model can be used to distinguish no arthritis, OA, and RA on hand radiographs with high performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Singh JA, Saag KG, Bridges SL, et al. 2015 American College of Rheumatology guideline for the treatment of rheumatoid arthritis. Arthritis Care Res. 2016;68(1):1–25. https://doi.org/10.1002/acr.22783.

    Article  Google Scholar 

  2. Smolen JS, Landewé RBM, Bijlsma JWJ, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update. Ann Rheum Dis. 2020;79(6):685–99. https://doi.org/10.1136/annrheumdis-2019-216655.

    Article  CAS  PubMed  Google Scholar 

  3. Lard LR, Visser H, Speyer I, et al. Early versus delayed treatment in patients with recent-onset rheumatoid arthritis: comparison of two cohorts who received different treatment strategies. Am J Med. 2001;111(6):446–51. https://doi.org/10.1016/s0002-9343(01)00872-5.

    Article  CAS  PubMed  Google Scholar 

  4. Hunter TM, Boytsov NN, Zhang X, Schroeder K, Michaud K, Araujo AB. Prevalence of rheumatoid arthritis in the United States adult population in healthcare claims databases, 2004-2014. Rheumatol Int. 2017;37(9):1551–7. https://doi.org/10.1007/s00296-017-3726-1.

    Article  PubMed  Google Scholar 

  5. Crowson CS, Matteson EL, Myasoedova E, et al. The lifetime risk of adult-onset rheumatoid arthritis and other inflammatory autoimmune rheumatic diseases. Arthritis Rheum. 2011;63(3):633–9. https://doi.org/10.1002/art.30155.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Birnbaum H, Pike C, Kaufman R, Marynchenko M, Kidolezi Y, Cifaldi M. Societal cost of rheumatoid arthritis patients in the US. Curr Med Res Opin. 2010;26(1):77–90. https://doi.org/10.1185/03007990903422307.

    Article  PubMed  Google Scholar 

  7. Leifer VP, Katz JN, Losina E. The burden of OA-health services and economics. Osteoarthr Cartil. 2022;30(1):10–6. https://doi.org/10.1016/j.joca.2021.05.007.

    Article  CAS  Google Scholar 

  8. Safiri S, Kolahi A-A, Smith E, et al. Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the Global Burden of Disease Study 2017. Ann Rheum Dis. 2020;79(6):819–28. https://doi.org/10.1136/annrheumdis-2019-216515.

    Article  PubMed  Google Scholar 

  9. Lawrence RC, Felson DT, Helmick CG, et al. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II. Arthritis Rheum. 2008;58(1):26–35. https://doi.org/10.1002/art.23176.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Litwic A, Edwards M, Dennison E, Cooper C. Epidemiology and burden of osteoarthritis. Br Med Bull. 2013;105:185–99. https://doi.org/10.1093/bmb/lds038.

    Article  PubMed  Google Scholar 

  11. Haugen IK, Englund M, Aliabadi P, et al. Prevalence, incidence and progression of hand osteoarthritis in the general population: the Framingham Osteoarthritis Study. Ann Rheum Dis. 2011;70(9):1581–6. https://doi.org/10.1136/ard.2011.150078.

    Article  PubMed  Google Scholar 

  12. Allen KD, Thoma LM, Golightly YM. Epidemiology of osteoarthritis. Osteoarthr Cartil. 2022;30(2):184–95. https://doi.org/10.1016/j.joca.2021.04.020.

    Article  CAS  Google Scholar 

  13. Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a Deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287(1):313–22. https://doi.org/10.1148/radiol.2017170236.

    Article  PubMed  Google Scholar 

  14. He Y, Pan I, Bao B, et al. Deep learning-based classification of primary bone tumors on radiographs: a preliminary study. eBioMedicine. Elsevier. 2020:62. https://doi.org/10.1016/j.ebiom.2020.103121.

  15. Carmo LO, van den Merkhof A, Olczak J, et al. An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics : are these externally validated and ready for clinical application? Bone Jt Open. 2021;2(10):879–85. https://doi.org/10.1302/2633-1462.210.BJO-2021-0133.

    Article  Google Scholar 

  16. Olsson S, Akbarian E, Lind A, Razavian AS, Gordon M. Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population. BMC Musculoskelet Disord. 2021;22(1):844. https://doi.org/10.1186/s12891-021-04722-7.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Murakami S, Hatano K, Tan J, Kim H, Aoki T. Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network. Multimed Tools Appl. 2018;77(9):10921–37. https://doi.org/10.1007/s11042-017-5449-4.

    Article  Google Scholar 

  18. Betancourt-Hernández M, Viera-López G, Serrano-Muñoz A. Automatic diagnosis of rheumatoid arthritis from hand radiographs using convolutional neural networks. Revista Cubana de Física, [S.l.]. 2018;35(1):39–43.

  19. Üreten K, Erbay H, Maraş HH. Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin Rheumatol. 2020;39(4):969–74. https://doi.org/10.1007/s10067-019-04487-4.

    Article  PubMed  Google Scholar 

  20. Üreten K, Maraş HH. Automated classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs with deep learning methods. J Digit Imaging. 2022;35(2):193–9. https://doi.org/10.1007/s10278-021-00564-w.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Tan M, Le QV. EfficientNet: rethinking model scaling for convolutional neural networks. arXiv; 2020. http://arxiv.org/abs/1905.11946. Accessed May 14, 2022.

  22. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009. p. 248–55. https://doi.org/10.1109/CVPR.2009.5206848.

  23. Rajpurkar P, Irvin J, Bagul A, et al. MURA: large dataset for abnormality detection in musculoskeletal radiographs. arXiv; 2018. http://arxiv.org/abs/1712.06957. Accessed May 14, 2022.

  24. Sabottke CF, Spieler BM. The effect of image resolution on deep learning in radiography. Radiology: Artificial Intelligence. Radiological Society of North America; 2020;2(1):e190015. https://doi.org/10.1148/ryai.2019190015.

    Book  Google Scholar 

  25. Hirano T, Nishide M, Nonaka N, et al. Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis. Rheumatol Adv Pract. 2019;3(2):rkz047. https://doi.org/10.1093/rap/rkz047.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fuchs HA, Kaye JJ, Callahan LF, Nance EP, Pincus T. Evidence of significant radiographic damage in rheumatoid arthritis within the first 2 years of disease. J Rheumatol. 1989;16(5):585–91.

    CAS  PubMed  Google Scholar 

  27. van der Heijde DM, van Leeuwen MA, van Riel PL, et al. Biannual radiographic assessments of hands and feet in a three-year prospective followup of patients with early rheumatoid arthritis. Arthritis Rheum. 1992;35(1):26–34. https://doi.org/10.1002/art.1780350105.

    Article  PubMed  Google Scholar 

  28. England BR. Clinical manifestations of rheumatoid arthritis. UpToDate. Waltham, MA; https://www.uptodate.com/contents/clinical-manifestations-of-rheumatoid-arthritis. Accessed June 26; 2022.

  29. Baker JF. Diagnosis and differential diagnosis of rheumatoid arthritis. UpToDate. Waltham, MA; https://www.uptodate.com/contents/diagnosis-and-differential-diagnosis-of-rheumatoid-arthritis. Accessed June 26; 2022.

  30. Bohndorf K, Schalm J. Diagnostic radiography in rheumatoid arthritis: benefits and limitations. Baillieres Clin Rheumatol. 1996;10(3):399–407. https://doi.org/10.1016/s0950-3579(96)80038-0.

    Article  CAS  PubMed  Google Scholar 

  31. Coffey CM, Crowson CS, Myasoedova E, Matteson EL, Davis JM. Evidence of diagnostic and treatment delay in seronegative rheumatoid arthritis: missing the window of opportunity. Mayo Clin Proc. 2019;94(11):2241–8. https://doi.org/10.1016/j.mayocp.2019.05.023.

    Article  CAS  PubMed  Google Scholar 

  32. Boeters DM, Gaujoux-Viala C, Constantin A, van der Helm-van Mil AHM. The 2010 ACR/EULAR criteria are not sufficiently accurate in the early identification of autoantibody-negative rheumatoid arthritis: results from the Leiden-EAC and ESPOIR cohorts. Semin Arthritis Rheum. 2017;47(2):170–4. https://doi.org/10.1016/j.semarthrit.2017.04.009.

    Article  PubMed  Google Scholar 

  33. Kay J, Upchurch KS. ACR/EULAR 2010 rheumatoid arthritis classification criteria. Rheumatology. 2012;51(suppl_6):vi5–9. https://doi.org/10.1093/rheumatology/kes279s.

    Article  PubMed  Google Scholar 

  34. McQueen FM. The use of MRI in early RA. Rheumatology (Oxford). 2008;47(11):1597–9. https://doi.org/10.1093/rheumatology/ken332.

    Article  CAS  PubMed  Google Scholar 

  35. Terslev L, Torp-Pedersen S, Savnik A, et al. Doppler ultrasound and magnetic resonance imaging of synovial inflammation of the hand in rheumatoid arthritis: a comparative study. Arthritis Rheum. 2003;48(9):2434–41. https://doi.org/10.1002/art.11245.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Yuntong Ma and Ian Pan contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA

    Yuntong Ma, Ian Pan, Stanley Y. Kim, Ged G. Wieschhoff, Katherine P. Andriole & Jacob C. Mandell

  2. MGH & BWH Center for Clinical Data Science, Suite 1303, 100 Cambridge St, Boston, MA, 02114, USA

    Katherine P. Andriole

Authors
  1. Yuntong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ian Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stanley Y. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ged G. Wieschhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Katherine P. Andriole
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jacob C. Mandell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuntong Ma.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, Y., Pan, I., Kim, S.Y. et al. Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography. Skeletal Radiol 53, 377–383 (2024). https://doi.org/10.1007/s00256-023-04408-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00256-023-04408-2

Keywords

Search

Navigation