Multiple Sclerosis Detection With Convolutional Neural Networks

Abstract

Introduction: Multiple sclerosis (MS) is characterized by increased neurodegeneration and inflammation, leading to long-term damage to the brainstem and central nervous system (CNS) and impairing neurological development. Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing and monitoring MS, as it reveals significant aspects of the disease’s pathology. However, challenges remain regarding prognosis, tracking disease progression, assessing CNS damage, and establishing differential diagnoses.

Objectives: The objective of this study is to explore machine learning (ML) techniques to address persistent challenges in MS diagnosis and monitoring. Specifically, we aim to enhance neurodegenerative characterization, improve prognostic subtyping, optimize imaging of critical brain pathology, and develop better lesion segmentation and diagnostic classification tools.

Methods: We conducted a thorough review and application of various machine learning approaches to the study of MS. Techniques were evaluated for their ability to support neurodegenerative assessment, differentiate MS from similar conditions, and improve lesion detection and segmentation. Emphasis was placed on enhancing model generalizability across different MRI scanners and patient populations. We also explored the development of user-friendly interfaces to ensure clinical applicability and precision validation by radiologists.

Results: Machine learning models demonstrated potential in aiding differential diagnosis, especially for radiologists without specialized neuroradiology expertise. While several models focus on distinguishing MS from non-muscular obstructive sleep disorder (NMOSD), broader clinical diagnostic challenges remain, particularly differentiating demyelinating lesions from vascular lesions. Limitations identified include the models’ generalizability to different MRI scanners and broader populations, as well as potential diagnostic tunnel vision due to narrow differential focus.

Conclusions: Although the use of machine learning tools for MS diagnosis is still in its early stages, they hold promise for aiding clinical practice. Future work must prioritize the robustness of ML models across varied MRI scanners, ensure broader applicability to diverse populations, and establish clear clinical thresholds for diagnostic outputs. Development of accessible interfaces that allow easy verification of results will be crucial for integrating these technologies into routine clinical workflows.