Detect Ankylosing Spondylitis Using Deep Learning: From Image Classification to Clinical Integration
Prof. Namrata Nagpure1, Gaurav Gadhave2, Apeksha Chaware3, Niraj Gore4, Rohit Mavale5
1Professor, Department of Information Technology and Data Science Engineering, JD College of Engineering and Management, Nagpur, Maharashtra, India
2,3,4,5Students, Department of Information Technology and Data Science Engineering, JD College of Engineering and Management, Nagpur, Maharashtra, India
Abstract— Ankylosing spondylitis (AS) is a chronic inflammatory disease primarily affecting the spine, leading to significant pain, stiffness, and eventual loss of mobility. Early detection and diagnosis of AS are crucial for preventing severe long-term effects and improving the quality of life for patients. Traditional diagnostic methods, such as radiographs and clinical assessments, are often limited in accuracy and efficiency, particularly in the early stages of the disease. In recent years, deep learning (DL) techniques have emerged as powerful tools in the medical imaging domain, providing automated and highly accurate diagnostic support. This review paper explores the role of deep learning in the detection and diagnosis of ankylosing spondylitis, focusing on the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced DL models. We examine various studies and methodologies that have leveraged deep learning to analyze medical imaging data, including X-rays, MRI scans, and CT scans, to detect AS-related abnormalities such as sacroiliitis, vertebral sclerosis, and spinal fusion. Additionally, the review highlights the advantages of using deep learning techniques over traditional methods, such as reduced diagnostic time, improved accuracy, and the ability to detect early-stage abnormalities that may be missed by human radiologists. The paper concludes with a discussion on the challenges and future directions in this field, including data availability, model generalization, and integration into clinical practice. Deep learning holds significant promise in revolutionizing the diagnostic process for ankylosing spondylitis, offering faster and more reliable assessments for improved patient outcomes.
Index Terms— Deep Neural Network, Ankylosing Spondylitis, Convolutional Neural Networks (CNNs), MRI Analysis, Smartphone-Based Applications, Data Availability, Ankylosing Spondylitis (AS)
