Machine LearningDeep Learning in Rheumatological Screening A Systematic Review
Year 2023,
Volume: 16 Issue: 3, 940 - 969, 31.12.2023
Zehra Aysun Altıkardeş
,
Emre Canayaz
,
Alparslan Ünsal
Abstract
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.
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Year 2023,
Volume: 16 Issue: 3, 940 - 969, 31.12.2023
Zehra Aysun Altıkardeş
,
Emre Canayaz
,
Alparslan Ünsal
References
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- 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.
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- 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.
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- 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.
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- 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.
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- 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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