A Review of Machine Learning Methods for Classification of Ankylosing Spondylitis Data

Document Type : Review articles

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Tropical-Biological Research Unit, College of Science, University of Baghdad, Baghdad, Iraq

Abstract

Ankylosing Spondylitis (AS) is a chronic inflammatory disease that primarily affects the spine and sacroiliac joints. Early diagnosis and treatment are crucial to preventing irreversible damage and improving patient outcomes. Early diagnosis of AS is critical yet difficult. Delays in diagnosis are widespread, typically lasting many years after symptoms first appear, due to the non-specific nature of early indicators and a lack of understanding among healthcare practitioners. Machine learning (ML) methods have shown great potential in classifying AS data, aiding in early detection and personalized treatment strategies. This paper aims to review the current literature on ML methods for AS classification, highlighting trends, challenges, and future directions in the field. It provides researchers with the latest insights into ML applications and encourages new ideas by analyzing image processing techniques utilized by these methods. In this systematic review, 43 articles are analyzed, focusing on the application of deep learning and machine learning techniques in diagnosing Ankylosing Spondylitis. The findings emphasize that ML methods significantly enhance segmentation accuracy and improve case classification for various rheumatologic diseases, including AS.

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