A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images

• Published in: Biocybernetics and biomedical engineering Vol. 41; no. 2; pp. 419 - 444
• Main Authors: Saini, Deepak, Chand, Trilok, Chouhan, Devendra K., Prakash, Mahesh
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2021
• Subjects: Advanced Basic Science; Computer aided diagnosis; Convolution neural networks; Deep learning; Internal Medicine; Knee osteoarthritis; Machine learning; Deep learning; Knee osteoarthritis; Computer aided diagnosis; Convolution neural networks; Machine learning
• ISSN: 0208-5216
• DOI: 10.1016/j.bbe.2021.03.002

The purpose of present review paper is to introduce the reader to key directions of manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from plain radiographs. This is a narrative review article in which we have described recent developments in severity evaluation of knee OA from X-ray images. We have primarily focussed on automatic analysis and have reviewed articles in which machine learning, transfer learning, active learning, etc. have been employed on X-ray images to access and classify the severity of knee OA. All original research articles on OA detection and classification using X-ray images published in English were searched on PubMed database, Google Scholar, RSNA radiology databases in year 2019. The search terms of “knee Osteoarthritis” were combined with search terms “Machine Learning”, ‘severity” and “X-ray”. The initial search on various publication databases revealed a total of 743 results, out of which only 26 articles were considered relevant to radiographic knee OA severity analysis. The majority of the articles were based on automatic analysis. Manual segmentation based articles were least in numbers. Computer aided methods to diagnose knee OA are great tools to detect OA at ealry stages. Advancements in Human Computer Interface systems have led the researchers to bridge the gap between machine learning algorithms and expert healthcare professionals to provide better and timely treatment options to the knee OA affected patients.