Document Type
Journal Article
Publication Title
Neuroscience Informatics
Publisher
Elsevier
School
Centre for Artificial Intelligence and Machine Learning (CAIML)
RAS ID
44402
Abstract
Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits.
DOI
10.1016/j.neuri.2022.100079
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Imtiaz, M., Shah, S. A. A., & ur Rahman, Z. (2022). A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges. Neuroscience Informatics, 2, Article 100079. https://doi.org/10.1016/j.neuri.2022.100079