Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning

Title

Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning

Creator

Hemanth K.S.; Tigulla D.; Lakshmi V.; Buhari S.

Description

This study explores the developing relationship between health care and technology, with a special emphasis on the use of machine learning (ML) algorithms to detect early stage osteoarthritis (OA) in the hip and knee joints. OA, a substantial worldwide health problem, requires improved diagnosis techniques. In this analysis, we illuminate the limitations of traditional methods, emphasizing the inherent subjectivity of clinical assessments and the delay in detection using routine imaging techniques. The research investigates the potential of ML to bring about significant changes. It focuses on combining various algorithms with extensive datasets and highlights the need to select relevant features and prepare the data to improve the accuracy of the models. The use of ML is closely connected to ethical issues, which include the protection of data privacy and the capacity to comprehend the models used. To bridge the gap between theory and practice, the chapter presents concrete examples of ML's practical use in detecting OA, opening possibilities for customized therapy and enhanced patient results. The chapter also highlights potential areas for future study, emphasizing the urgent requirement for additional progress in ML-based early detection techniques to alleviate the worldwide impact of OA. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Source

Diagnosing Musculoskeletal Conditions using Artifical Intelligence and Machine Learning to Aid Interpretation of Clinical Imaging, pp. 39-64.

Date

2024-01-01

Publisher

Elsevier

Subject

Data-driven approach; Early OA detection; Ethical considerations; Machine learning (ML); Traditional diagnosis limitations

Coverage

Hemanth K.S., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Tigulla D., REVA University, School of Computer Science and Applications, Karnataka, Bengaluru, India; Lakshmi V., REVA University, School of Computer Science and Applications, Karnataka, Bengaluru, India; Buhari S., Universiti Teknologi Brunei, School of Business, Bandar Seri Begawan, Brunei Darussalam

Rights

Restricted Access

Relation

ISBN: 978-044332892-3; 978-044332893-0

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-443-32892-3.00003-8

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213517235&doi=10.1016%2fB978-0-443-32892-3.00003-8&partnerID=40&md5=5feb555353195ee4f5dc8fa3c5c58d00