Skip to main content

Advertisement

SpringerLink
Log in

Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60 years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms. In this paper, a computer-aided system for helping in the RA diagnosis, based on quantitative and easy-to-acquire variables, is presented. The participants in this study were all female, grouped into two classes: class I, patients diagnosed with RA (n = 100), and class II corresponding to controls without RA (n = 100). The novel approach is constituted by the acquisition of thermal and RGB images, recording their hand grip strength or gripping force. The weight, height, and age were also obtained from all participants. The color layout descriptors (CLD) were obtained from each image for having a compact representation. After, a wrapper forward selection method in a range of classification algorithms included in WEKA was performed. In the feature selection process, variables such as hand images, grip force, and age were found relevant, whereas weight and height did not provide important information to the classification. Our system obtains an AUC ROC curve greater than 0.94 for both thermal and RGB images using the RandomForest classifier. Thirty-eight subjects were considered for an external test in order to evaluate and validate the model implementation. In this test, an accuracy of 94.7% was obtained using RGB images; the confusion matrix revealed our system provides a correct diagnosis for all participants and failed in only two of them (5.3%).

Graphical abstract

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Initiative C (2010) 2010 rheumatoid arthritis classification criteria. Arthritis Rheum 62:2569–2581

    Article  Google Scholar 

  2. Visser H, le Cessie S, Vos K, Breedveld FC, Hazes JMW (2002) How to diagnose rheumatoid arthritis early: a prediction model for persistent (erosive) arthritis. Arthritis Rheum 46:357–365

    Article  Google Scholar 

  3. Jung H-W, Lee S-H, Donnelley M, Parsons D, Lee I (2017) Automated detection of circular marker particles in synchrotron phase contrast X-ray images of live mouse nasal airways for mucociliary transit assessment. Expert Syst Appl 73:57–68

    Article  Google Scholar 

  4. Alamanos Y, Voulgari P V, Drosos AA (2006) Incidence and prevalence of rheumatoid arthritis, based on the 1987 American College of Rheumatology criteria: a systematic review. In: Seminars in arthritis and rheumatism. Elsevier, pp 182–188

  5. Barsky AJ, Peekna HM, Borus JF (2001) Somatic symptom reporting in women and men. J Gen Intern Med 16:266–275

    Article  CAS  Google Scholar 

  6. Mendoza-Vázquez G, Rocha-Muñoz AD, de Jesús G-SA et al (2013) Artritis reumatoide y dislipidemias. El Resid 8:12–22

    Google Scholar 

  7. Boeters DM, Gaujoux-Viala C, Constantin A, van der Helm-van AHM (2017) 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. In: Seminars in arthritis and rheumatism. Elsevier, pp 170–174

  8. Rindfleisch AJ, Muller D (2005) Diagnosis and management of rheumatoid arthritis. Am Fam Physician 72:1037–1047

    PubMed  Google Scholar 

  9. Chand AS, McHaffie A, Clarke AW, Reeves Q, Tan YM, Dalbeth N, Williams M, McQueen F (2011) Quantifying synovitis in rheumatoid arthritis using computer-assisted manual segmentation with 3 tesla MRI scanning. J Magn Reson Imaging 33:1106–1113

    Article  Google Scholar 

  10. Crowley AR, Dong J, McHaffie A, Clarke AW, Reeves Q, Williams M, Robinson E, Dalbeth N, McQueen FM (2011) Measuring bone erosion and edema in rheumatoid arthritis: a comparison of manual segmentation and RAMRIS methods. J Magn Reson Imaging 33:364–371

    Article  Google Scholar 

  11. Verstappen SMM, Jacobs JWG, Van der Veen MJ et al (2007) Intensive treatment with methotrexate in early rheumatoid arthritis: aiming for remission. Computer Assisted Management in Early Rheumatoid Arthritis (CAMERA, an open-label strategy trial). Ann Rheum Dis 66:1443–1449

    Article  CAS  Google Scholar 

  12. Sharp JT, Gardner JC, Bennett EM (2000) Computer-based methods for measuring joint space and estimating erosion volume in the finger and wrist joints of patients with rheumatoid arthritis. Arthritis Rheum 43:1378–1386

    Article  CAS  Google Scholar 

  13. Wilkinson SJ, Naylor A, Goh KL (2017) Digital image analysis protocol for determining the radiocarpal joint space in the rheumatoid arthritic wrist. Comput Biol Med 89:127–134

    Article  CAS  Google Scholar 

  14. Bridges AJ, Reid JC, Cutts JH III et al (1993) Ai/learn/rheumatology. A comparative study of computer-assisted instruction for rheumatology. Arthritis Rheum Off J Am Coll Rheumatol 36:577–580

    Article  CAS  Google Scholar 

  15. Umapathy S, Vasu S, Gupta N Computer aided diagnosis based hand thermal image analysis: a potential tool for the evaluation of rheumatoid arthritis. J Med Biol Eng 1–12

  16. Ring EFJ, Ammer K (2012) Infrared thermal imaging in medicine. Physiol Meas 33:R33–R46

    Article  CAS  Google Scholar 

  17. Snekhalatha U, Anburajan M, Sowmiya V, Venkatraman B, Menaka M (2015) Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis. Proc Inst Mech Eng Part H J Eng Med 229:319–331

    Article  CAS  Google Scholar 

  18. Fowler NK, Nicol AC (2001) Functional and biomechanical assessment of the normal and rheumatoid hand. Clin Biomech 16:660–666

    Article  CAS  Google Scholar 

  19. Helliwell P, Howe A, Wright V (1987) Functional assessment of the hand: reproducibility, acceptability, and utility of a new system for measuring strength. Ann Rheum Dis 46:203–208

    Article  CAS  Google Scholar 

  20. Cima SR, Barone A, Porto JM, de Abreu DCC (2013) Strengthening exercises to improve hand strength and functionality in rheumatoid arthritis with hand deformities: a randomized, controlled trial. Rheumatol Int 33:725–732

    Article  Google Scholar 

  21. Lee P, Baxter A, Dick WC, Webb J (1974) An assessment of grip strength measurement in rheumatoid arthritis. Scand J Rheumatol 3:17–23

    Article  CAS  Google Scholar 

  22. Guzmán IPG, Navarro-Zarza JE, Alonso-Silverio GA, et al (2018) AB0355 the relationship between hand prehensile strength, clinical activity and functional capacity in patients with rheumatoid arthritis

  23. Durmus E, Paker N, Bugdayci D, Goksenoglu G (2019) Determinants of the hand functions in patients with rheumatoid arthritis. Ann Med Res 26:1493–1497

    Google Scholar 

  24. Dedeoglu M, Gafuroglu U, Yilmaz O, Bodur H (2013) The relationship between hand grip and pinch strengths and disease activity, articular damage, pain, and disability in patients with rheumatoid arthritis: romatoid artritli hastalarda elle kavrama ve tutma guclerinin hastalik aktivitesi, eklem hasari, Agr. Turkish J Rheumatol 28:69–78

    Article  Google Scholar 

  25. Rydholm M, Book C, Wikström I, Jacobsson L, Turesson C (2018) Course of grip force impairment in patients with early rheumatoid arthritis over the first five years after diagnosis. Arthritis Care Res (Hoboken) 70:491–498

    Article  Google Scholar 

  26. Üreten K, Erbay H, Maraş HH (2020) Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin Rheumatol 39:969–974

    Article  Google Scholar 

  27. Hemalatha RJ, Vijaybaskar V, Thamizhvani TR (2019) Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning. Proc Inst Mech Eng Part H J Eng Med 233:657–667

    Article  CAS  Google Scholar 

  28. Kasutani E, Yamada A (2001) The MPEG-7 color layout descriptor: a compact image feature description for high-speed image/video segment retrieval. In: Image Processing, 2001. Proceedings. 2001 international conference on. IEEE, pp 674–677

  29. George Y, Aldeen M, & Garnavi R (2019) Automatic Scale Severity Assessment Method in Psoriasis Skin Images Using Local Descriptors. IEEE J Biomed Health Inform 24(2):577–585

  30. Vajda S, Karargyris A, Jaeger S, Santosh KC, Candemir S, Xue Z, Antani S, Thoma G (2018) Feature selection for automatic tuberculosis screening in frontal chest radiographs. J Med Syst 42:146

    Article  Google Scholar 

  31. Chen Q, Cheng G, Fang Y, et al (2018) Real-time learning-based monitoring system for water contamination. In: 2018 4th International Conference on Universal Village (UV). IEEE, pp 1–5

  32. Coimbra MT, Cunha JPS (2006) MPEG-7 visual descriptors—contributions for automated feature extraction in capsule endoscopy. IEEE Trans circuits Syst video Technol 16:628–637

    Article  Google Scholar 

  33. Kubicova V, Provaznik I (2016) Use of whole genome DNA spectrograms in bacterial classification. Comput Biol Med 69:298–307

    Article  CAS  Google Scholar 

  34. Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, Birnbaum NS, Burmester GR, Bykerk VP, Cohen MD, Combe B, Costenbader KH, Dougados M, Emery P, Ferraccioli G, Hazes JM, Hobbs K, Huizinga TW, Kavanaugh A, Kay J, Kvien TK, Laing T, Mease P, Menard HA, Moreland LW, Naden RL, Pincus T, Smolen JS, Stanislawska-Biernat E, Symmons D, Tak PP, Upchurch KS, Vencovsky J, Wolfe F, Hawker G (2010) 2010 rheumatoid arthritis classification criteria : an American College of Rheumatology / European League Against Rheumatism collaborative initiative. Ann Rheum Dis 69:1580–1588. https://doi.org/10.1136/ard.2010.138461

    Article  PubMed  Google Scholar 

  35. Nisbet R, Elder J, Miner G (2009) Handbook of statistical analysis and data mining applications. Academic Press, Amsterdam

  36. Aaland M (2007) Shooting digital: pro tips for taking great pictures with your digital camera. John Wiley & Sons, Hoboken

  37. Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf theory 8:179–187

    Google Scholar 

  38. Troncy R, Huet B, Schenk S (2011) Multimedia semantics: metadata, analysis and interaction. John Wiley & Sons, Hoboken

  39. Roberts HC, Denison HJ, Martin HJ, Patel HP, Syddall H, Cooper C, Sayer AA (2011) A review of the measurement of grip strength in clinical and epidemiological studies: towards a standardised approach. Age Ageing 40:423–429

    Article  Google Scholar 

  40. Fess EE (1981) Grip strength Clinical assessment recommendations. In: Casanova JS (ed) American Society of Hand Therapists. Chicago, pp 41–45

  41. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  42. Hsu H-H, Hsieh C-W, Lu M-D (2011) Hybrid feature selection by combining filters and wrappers. Expert Syst Appl 38:8144–8150

    Article  Google Scholar 

  43. Nalband S, Sundar A, Prince AA, Agarwal A (2016) Feature selection and classification methodology for the detection of knee-joint disorders. Comput Methods Prog Biomed 127:94–104

    Article  Google Scholar 

  44. Brea MLS, Rodríguez NB, Maroño NS et al (2016) On the development of conjunctival hyperemia computer-assisted diagnosis tools: influence of feature selection and class imbalance in automatic gradings. Artif Intell Med 71:30–42

    Article  Google Scholar 

  45. Alarcón-Paredes A, Alonso GA, Cabrera E, Cuevas-Valencia R (2017) Simultaneous gene selection and weighting in nearest neighbor classifier for gene expression data. Lecture Notes in Bioinformatics, In, pp 372–381

    Google Scholar 

  46. Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97:245–271

    Article  Google Scholar 

  47. Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. Aaai, In, pp 129–134

    Google Scholar 

  48. Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput C-26:917–922

    Article  Google Scholar 

  49. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. —Part II: More advanced machine learning schemes 4th ed. Morgan Kaufmann, Burlington

  50. Frank E, Hall M, Holmes G et al (2009) Weka-a machine learning workbench for data mining. Data mining and knowledge discovery handbook. Springer, In, pp 1269–1277

    Google Scholar 

  51. Ali A, Al-Ja’afari M, Abdulwahed S (2018) Rheumatoid arthritis diagnosis based on intelligent system. J Univ Babylon Pure Appl Sci 26:47–53

    Google Scholar 

Download references

Acknowledgments

The authors wish to sincerely thank the volunteer patients who participated in this study, as well to thank the following institutions for their support: Science and Technology National Council of Mexico, Universidad Autónoma de Guerrero, and Hospital General Dr. Raymundo Abarca Alarcón.

Funding

This work was financially supported by the Science and Technology National Council of Mexico in a way of scholarship for Diana E. Hernández-Rosales.

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de Guerrero, Chilpancingo, Mexico

    Antonio Alarcón-Paredes, Diana E. Hernández-Rosales, René E. Cuevas-Valencia & Gustavo A. Alonso

  2. Facultad de Ciencias Químico-Biológicas, Universidad Autónoma de Guerrero, Chilpancingo, Mexico

    Iris P. Guzmán-Guzmán

  3. Hospital General Dr. Raymundo Abarca Alarcón, Chilpancingo, Guerrero, Mexico

    José E. Navarro-Zarza

  4. Division of Medical Engineering Research, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Mexico City, Mexico

    Jessica Cantillo-Negrete

Authors
  1. Antonio Alarcón-Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iris P. Guzmán-Guzmán
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diana E. Hernández-Rosales
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José E. Navarro-Zarza
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jessica Cantillo-Negrete
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. René E. Cuevas-Valencia
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gustavo A. Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Antonio Alarcón-Paredes, Iris P. Guzmán-Guzmán, and Gustavo A. Alonso-Silverio. Data curation: Diana E. Hernández-Rosales and José E. Navarro-Zarza. Investigation: Diana E. Hernández-Rosales and José E. Navarro-Zarza. Methodology: Antonio Alarcón-Paredes and Gustavo A. Alonso-Silverio. Resources, René E. Cuevas-Valencia and Jessica Cantillo-Negrete. Software, Jessica Cantillo-Negrete and Diana E. Hernández-Rosales. Supervision: Iris P. Guzmán-Guzmán and José E. Navarro-Zarza. Validation: Diana E. Hernández-Rosales, Jessica Cantillo-Negrete, and René E. Cuevas-Valencia. Writing—original draft: Antonio Alarcón-Paredes and Gustavo A. Alonso-Silverio

Corresponding author

Correspondence to Gustavo A. Alonso.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alarcón-Paredes, A., Guzmán-Guzmán, I.P., Hernández-Rosales, D.E. et al. Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women. Med Biol Eng Comput 59, 287–300 (2021). https://doi.org/10.1007/s11517-020-02294-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02294-7

Keywords

Search

Navigation