Machine LearningDeep Learning in Rheumatological Screening A Systematic Review
Yıl 2023,
, 940 - 969, 31.12.2023
Zehra Aysun Altıkardeş
,
Emre Canayaz
,
Alparslan Ünsal
Öz
Machine learning and deep learning techniques have been used in many fields, especially automatic image processing techniques, in recent years. In light of these developments, it has become inevitable to develop applications in the medical field. This study focuses on the past few years of research using machine learning and deep learning methods in the context of image processing in the field of rheumatology. This review provides researchers with the latest information on the use of deep learning and machine learning and inspires them to generate new ideas in their research by analyzing image processing systems performed by these artificial intelligence methods. In the proposed systematic review, 28 articles covering the application of deep learning and machine learning methods in the domain of rheumatology with the aim of digital image processing in the last 18 years were evaluated. Experiments emphasize that machine learning and deep learning methods provide significant segmentation accuracy and better case classification accuracy for various rheumatologic diseases like rheumatoid arthritis, osteoarthritis, and ankylosing spondylitis. Lastly submitted review presents possible different research ideas for related researchers to concentrate on for their future studies.
Kaynakça
- Aizenberg, E., Roex, E. A., Nieuwenhuis, W. P., Mangnus, L., van der Helm‐van Mil, A. H., Reijnierse, M., . . . Stoel, B. C. (2018). Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: a feasibility study. Magnetic resonance in medicine, 79(2), 1127-1134.
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- Antony, J., McGuinness, K., O'Connor, N. E., & Moran, K. (2016). Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. Paper presented at the 2016 23rd International Conference on Pattern Recognition (ICPR).
- Ashinsky, B. G., Bouhrara, M., Coletta, C. E., Lehallier, B., Urish, K. L., Lin, P. C., . . . Spencer, R. G. (2017). Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative. Journal of Orthopaedic Research, 35(10), 2243-2250.
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- Bidgood Jr, W. D., Horii, S. C., Prior, F. W., & Van Syckle, D. E. (1997). Understanding and using DICOM, the data interchange standard for biomedical imaging. Journal of the American Medical Informatics Association, 4(3), 199-212.
- Boonen, A., Brinkhuizen, T., Landewé, R., van der Heijde, D., & Severens, J. L. (2010). Impact of ankylosing spondylitis on sick leave, presenteeism and unpaid productivity, and estimation of the societal cost. Annals of the Rheumatic Diseases, 69(6), 1123-1128. doi:10.1136/ard.2009.116764
- Boonen, A., & Mau, W. (2009). The economic burden of disease: comparison between rheumatoid arthritis and ankylosing spondylitis. Clinical and experimental rheumatology, 27(4 Suppl 55), S112-117.
- Brahim, A., Jennane, R., Riad, R., Janvier, T., Khedher, L., Toumi, H., & Lespessailles, E. (2019). A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative. Computerized Medical Imaging and Graphics, 73, 11-18.
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Yıl 2023,
, 940 - 969, 31.12.2023
Zehra Aysun Altıkardeş
,
Emre Canayaz
,
Alparslan Ünsal
Kaynakça
- Aizenberg, E., Roex, E. A., Nieuwenhuis, W. P., Mangnus, L., van der Helm‐van Mil, A. H., Reijnierse, M., . . . Stoel, B. C. (2018). Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: a feasibility study. Magnetic resonance in medicine, 79(2), 1127-1134.
- Algan, G., & Ulusoy, I. (2021). Image classification with deep learning in the presence of noisy labels: A survey. Knowledge-Based Systems, 215, 106771.
- Antony, J., McGuinness, K., O'Connor, N. E., & Moran, K. (2016). Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks. Paper presented at the 2016 23rd International Conference on Pattern Recognition (ICPR).
- Ashinsky, B. G., Bouhrara, M., Coletta, C. E., Lehallier, B., Urish, K. L., Lin, P. C., . . . Spencer, R. G. (2017). Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative. Journal of Orthopaedic Research, 35(10), 2243-2250.
- Avramidis, G. P., Avramidou, M. P., & Papakostas, G. A. (2022). Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane. Applied Sciences, 12(1), 10.
- Becker, A. (2019). Artificial intelligence in medicine: What is it doing for us today? Health Policy and Technology, 8(2), 198-205.
- Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), 1-41.
- Bidgood Jr, W. D., Horii, S. C., Prior, F. W., & Van Syckle, D. E. (1997). Understanding and using DICOM, the data interchange standard for biomedical imaging. Journal of the American Medical Informatics Association, 4(3), 199-212.
- Boonen, A., Brinkhuizen, T., Landewé, R., van der Heijde, D., & Severens, J. L. (2010). Impact of ankylosing spondylitis on sick leave, presenteeism and unpaid productivity, and estimation of the societal cost. Annals of the Rheumatic Diseases, 69(6), 1123-1128. doi:10.1136/ard.2009.116764
- Boonen, A., & Mau, W. (2009). The economic burden of disease: comparison between rheumatoid arthritis and ankylosing spondylitis. Clinical and experimental rheumatology, 27(4 Suppl 55), S112-117.
- Brahim, A., Jennane, R., Riad, R., Janvier, T., Khedher, L., Toumi, H., & Lespessailles, E. (2019). A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative. Computerized Medical Imaging and Graphics, 73, 11-18.
- Bressem, K. K., Vahldiek, J. L., Adams, L. C., Niehues, S. M., Haibel, H., Rodriguez, V. R., . . . Poddubnyy, D. (2020). Detecting radiographic sacroiliitis using deep learning with expert-level accuracy in axial spondyloarthritis. medRxiv, 2020.2005.2019.20105304. doi:10.1101/2020.05.19.20105304
- Brui, E., Efimtcev, A. Y., Fokin, V. A., Fernandez, R., Levchuk, A. G., Ogier, A. C., . . . Bendahan, D. (2020). Deep learning‐based fully automatic segmentation of wrist cartilage in MR images. NMR in Biomedicine, 33(8), e4320.
- Carano, R. A., Lynch, J. A., Redei, J., Ostrowitzki, S., Miaux, Y., Zaim, S., . . . Genant, H. K. (2004). Multispectral analysis of bone lesions in the hands of patients with rheumatoid arthritis. Magnetic resonance imaging, 22(4), 505-514.
- Castro-Zunti, R., Park, E. H., Choi, Y., Jin, G. Y., & Ko, S.-b. (2020). Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis. Computerized Medical Imaging and Graphics, 82, 101718. doi:https://doi.org/10.1016/j.compmedimag.2020.101718
- Chaturvedi, N. (2021). Deepra: Predicting joint damage from radiographs using cnn with attention. arXiv preprint arXiv:2102.06982.
- Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., . . . Williams, J. (2013). Recent advances in deep learning for speech research at Microsoft. Paper presented at the 2013 IEEE international conference on acoustics, speech and signal processing.
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
- Esses, S. J., Lu, X., Zhao, T., Shanbhogue, K., Dane, B., Bruno, M., & Chandarana, H. (2018). Automated image quality evaluation of T2‐weighted liver MRI utilizing deep learning architecture. Journal of Magnetic Resonance Imaging, 47(3), 723-728.
- Faleiros, M. C., Nogueira-Barbosa, M. H., Dalto, V. F., Júnior, J. R. F., Tenório, A. P. M., Luppino-Assad, R., . . . de Azevedo-Marques, P. M. (2020). Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging. Advances in Rheumatology, 60, 1-10.
- Fiorentino, M. C., Moccia, S., Cipolletta, E., Filippucci, E., & Frontoni, E. (2019). A learning approach for informative-frame selection in US rheumatology images. Paper presented at the International Conference on image analysis and processing.
- Golkov, V., Dosovitskiy, A., Sperl, J. I., Menzel, M. I., Czisch, M., Sämann, P., . . . Cremers, D. (2016). Q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE transactions on medical imaging, 35(5), 1344-1351.
- Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36-S40.
- Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., & Knoll, F. (2018). Learning a variational network for reconstruction of accelerated MRI data. Magnetic resonance in medicine, 79(6), 3055-3071.
- Han, Q., Lu, Y., Han, J., Luo, A., Huang, L., Ding, J., . . . Liang, Q. (2021). Automatic quantification and grading of hip bone marrow oedema in ankylosing spondylitis based on deep learning. Modern Rheumatology.
- Hemalatha, R., Vijaybaskar, V., & Thamizhvani, T. (2019). Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 233(6), 657-667.
- Hirano, T., Nishide, M., Nonaka, N., Seita, J., Ebina, K., Sakurada, K., & Kumanogoh, A. (2019). Development and validation of a deep-learning model for scoring of radiographic finger joint destruction in rheumatoid arthritis. Rheumatology advances in practice, 3(2), rkz047.
- Hirvasniemi, J., Gielis, W. P., Arbabi, S., Agricola, R., van Spil, W. E., Arbabi, V., & Weinans, H. (2019). Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study. Osteoarthritis and cartilage, 27(6), 906-914.
- Kansagra, A. P., John-Paul, J. Y., Chatterjee, A. R., Lenchik, L., Chow, D. S., Prater, A. B., . . . Heilbrun, M. E. (2016). Big data and the future of radiology informatics. Academic radiology, 23(1), 30-42.
- Kayalibay, B., Jensen, G., & van der Smagt, P. (2017). CNN-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056.
- Khan, M. A. (2002). Thoughts concerning the early diagnosis of ankylosing spondylitis and related diseases. Clinical and experimental rheumatology, 20(6 Suppl 28), S6-10. Retrieved from http://europepmc.org/abstract/MED/12463439
- Kim, K.-J., & Tagkopoulos, I. (2019). Application of machine learning in rheumatic disease research. The Korean journal of internal medicine, 34(4), 708-722. doi:10.3904/kjim.2018.349
- Knight, W. (2017). The dark secret at the heart of AI'11 April 2017. In: MIT Technology Review https://www. technologyreview. com/s/604087/the-dark ….
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