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REVIEW ARTICLE

Tracking neurodegeneration in multiple sclerosis: the role of optical coherence tomography, optical coherence tomography angiography, and artificial intelligence-based imaging

Elham Moases Ghaffary1* ,  Jalal Rostampour2 ,  Omid Mirmosayyeb3
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1 Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri, United States of America
2 Biomedical Sciences Department, School of Health Sciences, Missouri State University, Springfield, Missouri, United States of America
3 Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York, United States of America
Advanced Neurology, 025440106 https://doi.org/10.36922/AN025440106
Received: 30 October 2025 | Revised: 18 December 2025 | Accepted: 25 December 2025 | Published online: 20 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Multiple sclerosis (MS) represents a chronic autoimmune disease involving the central nervous system through inflammation and progressive neurodegeneration. Monitoring disease progression, specifically in terms of neuroaxonal loss, has critical importance in guiding treatment and improving patient outcomes. This review discusses the evolving role of optical coherence tomography (OCT) and artificial intelligence (AI) in monitoring MS progression, highlighting their efficiency in assessing neuroaxonal damage, predicting disability, and complementing conventional imaging techniques. We synthesize current evidence on OCT and OCT angiography (OCTA) metrics, including peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell/inner plexiform layer (GCIPL) thickness, and their correlations with disability, cognitive impairment, magnetic resonance imaging (MRI) findings, and clinical progression. We further review machine learning and deep learning applications for OCT-based classification, segmentation, and prognosis in MS. Thinning of pRNFL and GCIPL correlates with higher expanded disability status scale, brain atrophy, and impaired cognition, often preceding clinical signs of progression. OCTA reveals reduced retinal capillary density in MS, supporting a vascular component in disease pathology. AI tools enhance OCT analysis through automated layer segmentation and predictive modeling, enabling individualized risk stratification and early detection of secondary progression. Multimodal AI frameworks that combine OCT with MRI and clinical data further enhance prognostic accuracy. In summary, OCT and AI are transforming MS disease progression tracking by providing affordable, non-invasive biomarkers reflecting neurodegeneration and cerebrovascular dysfunction. For achieving their full potential as predictive and therapeutic tools in individualized care for MS patients, standardization, external validation, and integration into clinical practice are essential.

Graphical abstract
Keywords
Multiple sclerosis
Optical coherence tomography
Optical coherence tomography angiography
Artificial intelligence
Imaging
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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Advanced Neurology, Electronic ISSN: 2810-9619 Print ISSN: 3060-8589, Published by AccScience Publishing
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