Radiograph-based rheumatoid arthritis diagnosis via convolutional neural network.
Computer-aided diagnosis
Convolutional neural network
Hand radiograph
Rheumatoid arthritis
Journal
BMC medical imaging
ISSN: 1471-2342
Titre abrégé: BMC Med Imaging
Pays: England
ID NLM: 100968553
Informations de publication
Date de publication:
22 Jul 2024
22 Jul 2024
Historique:
received:
17
05
2024
accepted:
11
07
2024
medline:
23
7
2024
pubmed:
23
7
2024
entrez:
22
7
2024
Statut:
epublish
Résumé
Rheumatoid arthritis (RA) is a severe and common autoimmune disease. Conventional diagnostic methods are often subjective, error-prone, and repetitive works. There is an urgent need for a method to detect RA accurately. Therefore, this study aims to develop an automatic diagnostic system based on deep learning for recognizing and staging RA from radiographs to assist physicians in diagnosing RA quickly and accurately. We develop a CNN-based fully automated RA diagnostic model, exploring five popular CNN architectures on two clinical applications. The model is trained on a radiograph dataset containing 240 hand radiographs, of which 39 are normal and 201 are RA with five stages. For evaluation, we use 104 hand radiographs, of which 13 are normal and 91 RA with five stages. The CNN model achieves good performance in RA diagnosis based on hand radiographs. For the RA recognition, all models achieve an AUC above 90% with a sensitivity over 98%. In particular, the AUC of the GoogLeNet-based model is 97.80%, and the sensitivity is 100.0%. For the RA staging, all models achieve over 77% AUC with a sensitivity over 80%. Specifically, the VGG16-based model achieves 83.36% AUC with 92.67% sensitivity. The presented GoogLeNet-based model and VGG16-based model have the best AUC and sensitivity for RA recognition and staging, respectively. The experimental results demonstrate the feasibility and applicability of CNN in radiograph-based RA diagnosis. Therefore, this model has important clinical significance, especially for resource-limited areas and inexperienced physicians.
Identifiants
pubmed: 39039460
doi: 10.1186/s12880-024-01362-w
pii: 10.1186/s12880-024-01362-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
180Subventions
Organisme : Medical Scientific Research Foundation of Zhejiang Province, China
ID : 2021KY1008
Organisme : Medical Scientific Research Foundation of Zhejiang Province, China
ID : 2021KY301
Organisme : Ningbo Health Technology Project,China
ID : 2023Y19
Organisme : Key discipline Foundation of Ningbo No.2 Hospital, China
ID : 2023-Y04
Informations de copyright
© 2024. The Author(s).
Références
Scherer HU, Häupl T, Burmester GR. The etiology of rheumatoid arthritis. J Autoimmun. 2020;110:102400.
doi: 10.1016/j.jaut.2019.102400
pubmed: 31980337
Weyand CM, Goronzy JJ. The immunology of rheumatoid arthritis. Nat Immunol. 2021;22(1):10–8.
doi: 10.1038/s41590-020-00816-x
pubmed: 33257900
Dougados M. Comorbidities in rheumatoid arthritis. Curr Opin Rheumatol. 2016;28(3):282–8.
doi: 10.1097/BOR.0000000000000267
pubmed: 27027814
McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med. 2011;365(23):2205–19.
doi: 10.1056/NEJMra1004965
pubmed: 22150039
Conforti A, Di Cola I, Pavlych V, Ruscitti P, Berardicurti O, Ursini F, et al. Beyond the joints, the extra-articular manifestations in rheumatoid arthritis. Autoimmun Rev. 2021;20(2):102735.
doi: 10.1016/j.autrev.2020.102735
pubmed: 33346115
Finckh A, Gilbert B, Hodkinson B, Bae SC, Thomas R, Deane KD, et al. Global epidemiology of rheumatoid arthritis. Nat Rev Rheumatol. 2022;18(10):591–602.
pubmed: 36068354
Josef SS, Daniel A. Rheumatoid Arthritis. Lancet. 2016;388(10055):2023–38.
doi: 10.1016/S0140-6736(16)30173-8
Aletaha D, Smolen JS. Diagnosis and management of rheumatoid arthritis: a review. JAMA. 2018;320(13):1360–72.
doi: 10.1001/jama.2018.13103
pubmed: 30285183
Cush JJ. Rheumatoid arthritis: early diagnosis and treatment. Rheum Dis Clin. 2022;48(2):537–47.
doi: 10.1016/j.rdc.2022.02.010
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
doi: 10.1038/nature14539
pubmed: 26017442
Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst. 2021.
Chen X, Wang X, Zhang K, Fung KM, Thai TC, Moore K, et al. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022;79:102444.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.
doi: 10.1145/3065386
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas: IEEE; 2016. p. 770–778.
Honda S, Yano K, Tanaka E, Ikari K, Harigai M. Development of a scoring model for the Sharp/van der Heijde score using convolutional neural networks and its clinical application. Rheumatology. 2023;62(6):2272–83.
doi: 10.1093/rheumatology/keac586
pubmed: 36218391
Cao H, Liu H, Song E, Ma G, Xu X, Jin R, et al. A two-stage convolutional neural networks for lung nodule detection. IEEE J Biomed Health Inform. 2020;24(7):2006–15.
pubmed: 31905154
Gu D, Liu G, Xue Z. On the performance of lung nodule detection, segmentation and classification. Comput Med Imaging Graph. 2021;89:101886.
doi: 10.1016/j.compmedimag.2021.101886
pubmed: 33706112
Bakkouri I, Afdel K. Convolutional neural-adaptive networks for melanoma recognition. In: Image and Signal Processing: 8th International Conference, ICISP 2018, Cherbourg, France, July 2-4, 2018, Proceedings 8. Springer; 2018. pp. 453–460.
Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans Med Imaging. 2018;37(8):1822–34.
doi: 10.1109/TMI.2018.2806309
pubmed: 29994628
pmcid: 6076994
Bakkouri I, Bakkouri S. 2MGAS-Net: multi-level multi-scale gated attentional squeezed network for polyp segmentation. SIViP. 2024;18:5377–86. https://doi.org/10.1007/s11760-024-03240-y .
Zhao Y, Li H, Wan S, Sekuboyina A, Hu X, Tetteh G, et al. Knowledge-aided convolutional neural network for small organ segmentation. IEEE J Biomed Health Inform. 2019;23(4):1363–73.
doi: 10.1109/JBHI.2019.2891526
pubmed: 30629519
Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954–61.
doi: 10.1038/s41591-019-0447-x
pubmed: 31110349
Shen Y, Wu N, Phang J, Park J, Liu K, Tyagi S, et al. An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Med Image Anal. 2021;68:101908.
doi: 10.1016/j.media.2020.101908
pubmed: 33383334
Yu H, Yang LT, Zhang Q, Armstrong D, Deen MJ. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing. 2021;444:92–110.
doi: 10.1016/j.neucom.2020.04.157
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv preprint arXiv:1409.1556 .
Hioki Y, Makino K, Koyama K, Haro H, Terada H. Evaluation method of rheumatoid arthritis by the X-ray photograph using deep learning. In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech). IEEE; 2021. pp. 444–447.
Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):1–48.
doi: 10.1186/s40537-019-0197-0
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Boston: IEEE; 2015. pp. 1–9.
Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR; 2019. pp. 6105–6114.
Chang PD, Wong TT, Rasiej MJ. Deep learning for detection of complete anterior cruciate ligament tear. J Digit Imaging. 2019;32:980–6.
doi: 10.1007/s10278-019-00193-4
pubmed: 30859341
pmcid: 6841825
Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11):2579–605.
Singla S, Eslami M, Pollack B, Wallace S, Batmanghelich K. Explaining the black-box smoothly–a counterfactual approach. Med Image Anal. 2023;84:102721.
doi: 10.1016/j.media.2022.102721
pubmed: 36571975
Zhang C, Peng J, Wang L, Wang Y, Chen W, Sun Mw, et al. A deep learning-powered diagnostic model for acute pancreatitis. BMC Med Imaging. 2024;24(1):154.
Morita K, Tashita A, Nii M, Kobashi S. Computer-aided diagnosis system for rheumatoid arthritis using machine learning. In: 2017 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2. IEEE; 2017. pp. 357–360.
Üreten K, Erbay H, Maraş HH. Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clin Rheumatol. 2020;39:969–74.
doi: 10.1007/s10067-019-04487-4
pubmed: 30850962
Mate GS, Kureshi AK, Singh BK. An efficient CNN for hand X-ray classification of rheumatoid arthritis. J Healthc Eng. 2021;2021:6712785.