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Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network

  • Kemal ÜretenEmail author
  • Hasan Erbay
  • Hadi Hakan Maraş
Original Article
Part of the following topical collections:
  1. Artificial Intelligence and Machine Learning for Clinicians

Abstract

Introduction

Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis.

Methods

A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA.

Results

The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500.

Conclusion

Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.

Keywords

Convolutional neural network Deep learning Plain hand radiographs Rheumatoid arthritis 

Notes

Compliance with ethical standards

Disclosures

None.

Ethics approval

Ethical approval of this study was obtained from the Kırıkkale University Non-Interventional Research Ethics Committee on Dec 2, 2018, no. 2018.09.02.

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Copyright information

© International League of Associations for Rheumatology (ILAR) 2019

Authors and Affiliations

  1. 1.Department of Rheumatology, Faculty of MedicineKırıkkale UniversityKırıkkaleTurkey
  2. 2.Department of Computer Engineering (MSc)Çankaya UniversityAnkaraTurkey
  3. 3.Department of Computer Engineering, Faculty of EngineeringKırıkkale UniversityKırıkkaleTurkey
  4. 4.Department of Computer Engineering, Faculty of EngineeringÇankaya UniversityAnkaraTurkey

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