Open Access Open Access  Restricted Access Subscription Access

Arthritis Detection using AI

Suneetha J, Prasanjit Singh, Krishmal Shrestha, Abhijeet Singh, Pallavi Bhagat

Abstract


Arthritis is a prevalent condition that affects the majority of people after they reach a certain age. It's more than just ligament wear and tear. There are around 200 conditions that affect the joints, surrounding tissues, and other connective tissue. It's a rheumatic disease. We can apply Artificial Intelligence and follow particular methods to diagnose this disease in its early stages. In today's world, artificial intelligence is the most profitable field. It is currently used in the majority of fields. One of the most obvious fields where AI can be applied for human benefit is medicine. In the medical field, AI could be applied in a variety of fields, including cancer detection, tumours, and heart disease. Arthritis is another medical condition where AI can be helpful. As a result, we employed AI (Deep Learning) to detect Arthritis at an earlier stage with more accuracy.

Full Text:

PDF

References


Dang, S. D. H., & Allison, L. (2020, August). Using Deep Learning To Assign Rheumatoid Arthritis Scores. In 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI) (pp. 399-402). IEEE.

Nouri, A., Amirfattahi, R., & Moussavi, H. (2016, May). Mutual information based detection of thermal profile in hand joints of rheumatoid arthritis patients using Non-parametric windows. In 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-4). IEEE.

Morita, K., Tashita, A., Nii, M., & Kobashi, S. (2017, July). Computer-aided diagnosis system for rheumatoid arthritis using machine learning. In 2017 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 357-360). IEEE.

Ali-Eldin, A. M., Hafez, E. A., & Helal, R. (2017, December). Towards an automated approach for health status evaluation of patients with Juvenile Idiopathic Arthritis. In 2017 12th International Conference on Computer Engineering and Systems (ICCES) (pp. 540-543). IEEE.

Huo, Y., Vincken, K. L., van der Heijde, D., De Hair, M. J., Lafeber, F. P., & Viergever, M. A. (2015). Automatic quantification of radiographic finger joint space width of patients with early rheumatoid arthritis. IEEE Transactions on Biomedical Engineering, 63(10), 2177-2186.

Huo, Y., Vincken, K. L., Viergever, M. A., & Lafeber, F. P. (2013, April). Automatic joint detection in rheumatoid arthritis hand radiographs. In 2013 IEEE 10th International Symposium on Biomedical Imaging (pp. 125-128). IEEE.

Subramoniam, M. (2015, March). A non-invasive method for analysis of arthritis inflammations by using image segmentation algorithm. In 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] (pp. 1-4). IEEE.

Majumdar, P., Das, K., Nath, N., & Bhowmik, M. K. (2018, June). Detection of Inflammation from temperature profile using Arthritis knee joint Datasets. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 409-411). IEEE.

Bhisikar, S. A., & Kale, S. N. (2016, December). Automatic joint detection and measurement of joint space width in arthritis. In 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT) (pp. 429-432). IEEE.

Singh, U. V., Gupta, E., & Choudhury, T. (2019, December). Detection of Rheumatoid Arthritis Using Machine Learning. In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 25-29). IEEE.


Refbacks

  • There are currently no refbacks.