Survey of Ankylosing Spondylitis for Biomedical Imaging with Deep Neural Networks

Ankylosing Spondylitis (AS) is a chronic inflammat ory disease that affects the axial spine and peripheral joints, manifesting through symptoms such as spinal stiffness, back pain, and joint involvement. Medical imaging, particularly X-rays and MRIs, is essential for diagnosing AS and monitoring its progression. Recent advancements in artificial intelligence (AI), especially deep learning techniques, have significantly enhanced AS diagnosis by enabling the automated analysis of medical images to identify subtle patterns and markers indicative of the disease. This paper introduces an innovative framework that integrates deep learning with Explainable AI (XAI) techniques to improve the diagnostic process for AS. Our deep learning model, trained on an extensive dataset of MRI scans, demonstrates high accuracy and robustness in detecting AS. Nevertheless, the inherent complexity of deep learning models, often described as “black box” models, raises concerns about their interpretability and transparency in clinical decision-making. To address these challenges, we incorporate XAI methods such as Grad-CAM and LIME, which provide visual and feature-based explanations of the model's predictions. These techniques enhance the interpretability of the model, allowing clinicians to understand and trust AI-driven decisions. The novelty of our approach lies in the seamless integration of XAI methods with deep learning for AS diagnosis, which not only improves diagnostic accuracy but also ensures compliance with ethical and legal standards in healthcare. Continued research and collaboration between AI experts and healthcare professionals are crucial to fully realizing the potential of deep learning and XAI in AS diagnosis and to ultimately enhance patient care.