Multiple Sclerosis Diagnosis: Unraveling the Power of MRI and Advanced Technologies for Precise Detection

In the realm of multiple sclerosis (MS) diagnosis, the integration of cutting-edge technologies, particularly magnetic resonance imaging (MRI), has paved the way for unprecedented advancements. This paper delves into a groundbreaking method that seeks to revolutionize the diagnosis of MS by unraveling the distinctive power of MRI, with a particular focus on the spinal cord. The primary objective of this study is to discern and classify different stages of MS, specifically distinguishing between advanced and earlier stages. To achieve this classification, our approach places exclusive emphasis on information extracted from the patients' spinal cord as imaged through MRI. The rationale behind this emphasis lies in the pivotal role the spinal cord plays in the progression and manifestation of MS, making it a region of paramount significance for accurate diagnosis. Our method capitalizes on state-of-the-art techniques, including deep learning models, which have demonstrated exceptional capabilities in various medical imaging domains. Leveraging insights from prior research, particularly in brain MRI segmentation and lesion detection in MS, we integrate these findings into a novel framework tailored for spinal cord analysis. This approach is designed to provide a nuanced understanding of the disease's evolution, offering clinicians a reliable tool to discriminate between advanced and earlier stages of MS based solely on spinal cord MRI data. Additionally, the paper explores the potential of synthetic imaging generated through deep learning, enhancing the detection of MS lesions. By incorporating advanced technologies and methodologies, we aim to contribute to the refinement of diagnostic criteria, aligning with the evolving landscape of MS research. Ultimately, this study aspires to foster a paradigm shift in the precision and efficiency of MS diagnosis, emphasizing the pivotal role of spinal cord analysis through innovative MRI-based classification methodologies.

کلیدواژه ها:

Multiple Sclerosis (MS) ، Magnetic Resonance Imaging (MRI) ، Neuroimaging ، Neural Networks ، Machine Learning(ML)