Kemal Üreten1,2, Hasan Erbay3, Hadi Hakan Maraş4. 1. Department of Rheumatology, Faculty of Medicine, Kırıkkale University, 71450, Kırıkkale, Turkey. kemalureten@yahoo.com. 2. Department of Computer Engineering (MSc), Çankaya University, Ankara, Turkey. kemalureten@yahoo.com. 3. Department of Computer Engineering, Faculty of Engineering, Kırıkkale University, Kırıkkale, Turkey. 4. Department of Computer Engineering, Faculty of Engineering, Çankaya University, Ankara, Turkey.
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.
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.
Entities:
Keywords:
Convolutional neural network; Deep learning; Plain hand radiographs; Rheumatoid arthritis
Authors: Lukas Folle; David Simon; Koray Tascilar; Gerhard Krönke; Anna-Maria Liphardt; Andreas Maier; Georg Schett; Arnd Kleyer Journal: Front Med (Lausanne) Date: 2022-03-10
Authors: Thomas Dratsch; Michael Korenkov; David Zopfs; Sebastian Brodehl; Bettina Baessler; Daniel Giese; Sebastian Brinkmann; David Maintz; Daniel Pinto Dos Santos Journal: Eur Radiol Date: 2020-09-28 Impact factor: 5.315