ReviewCaspian journal of internal medicine2026
Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.
Review in Caspian journal of internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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