Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.
Authors
Affiliations (2)
Affiliations (2)
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
- Sevom Shaban Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract
Multiple sclerosis (MS) is an autoimmune disease that affects various parts of the central nervous system and often occurs in young population (between 20-40 years old). Given that MS is a lifelong disease and there is currently no definitive treatment for MS, early diagnosis, initiation of treatment with the most appropriate medication, and patient monitoring are three challenging factors in determining the status of MS patients. Magnetic resonance imaging (MRI) and optical coherence tomography (OCT) are two important and useful imaging methods in all the three aspects of diagnosing, monitoring, and determining the effectiveness of treatment in MS patients. In recent years, the use of artificial intelligence in analyzing MRI and OCT data in these aspects has been rapidly increasing. In this article, we reviewed and discussed the usage of deep learning as a class of machine learning and a method of artificial intelligence for analyzing data obtained from MRI and OCT in MS patients.