Retinal imaging with optical coherence tomography in multiple sclerosis: novel aspects

Effect of Different Treatments on Retinal Thickness Changes in Patients With Multiple Sclerosis: A Review

BACKGROUND

Multiple sclerosis (MS) is an autoimmune disorder affecting the central nervous system, with varying clinical manifestations such as optic neuritis, sensory disturbances, and brainstem syndromes. Disease progression is monitored through methods like MRI scans, disability scales, and optical coherence tomography (OCT), which can detect retinal thinning, even in the absence of optic neuritis. MS progression involves neurodegeneration, particularly trans-synaptic degeneration, which extends beyond the initial injury site. This review focuses on the impact of different MS treatments on retinal thickness as assessed by OCT.

RESULTS

Injectable drugs, such as interferon beta and glatiramer acetate (GA), have a relatively modest impact on retinal atrophy. Oral medications like Fingolimod, Teriflunomide, and Dimethyl fumarate also have different impacts on retinal thickness. Fingolimod has been shown to protect against retinal thinning but may lead to macular edema. DMF-treated patients had less ganglion cell-inner plexiform layer thinning than GA-treated patients but more thinning compared to natalizumab-treated patients and healthy controls. Teriflunomide's impact on retinal layers remains unexplored in human studies. Monoclonal antibodies, including Alemtuzumab, Rituximab, Ocrelizumab, and Natalizumab, had protective effects on retinal layer atrophy. Alemtuzumab-treated patients showed significantly less atrophy compared to interferon- and GA-treated patients. Rituximab initially increased atrophy rates in the first months but subsequently demonstrated potential neuroprotective effects. Ocrelizumab slowed the rate of inner nuclear layer thinning in progressive forms of the disease. Natalizumab is considered the most effective in reducing retinal layer atrophy, particularly the peripapillary retinal nerve fiber layer.

CONCLUSIONS

It's important to note that the effectiveness of these treatments may vary depending on MS subtype and individual factors. Future research should explore the long-term effects of these treatments on retinal layers and their correlations with overall disease progression and disability in MS patients.

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Swept-Source Optical Coherence Tomography Thresholds in Differentiating Clinical Outcomes in a Real-World Cohort of Treatment-Naïve Multiple Sclerosis Patients

This study aimed to determine whether peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell–inner plexiform layer (GCIPL) thickness thresholds for single-time-point swept-source optical coherence tomography (SS-OCT) measures can differentiate the clinical outcomes of treatment-naïve people with multiple sclerosis (pwMS). A total of 275 patients with the clinically isolated syndrome (n = 23), benign MS (n = 8), relapsing–remitting MS (n = 185), secondary progressive MS (n = 28), primary progressive MS (n = 31), and with no history of optic neuritis were included. The mean Expanded Disability Status Scale (EDSS) score was 3.0 ± 1.6. The cut-off values of pRNFL (87 µm and 88 µm) and GCIPL (70 µm) thicknesses have been adopted from previous studies using spectral-domain OCT. PwMS with pRNFL ≤87 µm and ≤88 µm had a longer disease duration, more advanced disability, and more frequently progressive MS variants compared to those with greater pRNFL thicknesses. In distinguishing pwMS with disability greater than or equal to the mean EDSS score (EDSS ≥ 3) from those with less severe disability, GCIPL thickness <70 µm had the highest sensitivity, while pRNFL thickness ≤87 µm had the greatest specificity. The optimal cut-off values differentiating patients with EDSS ≥ 3 from those with less severe disability was 63 µm for GCIPL thickness and 93.5 µm for pRNFL thickness. In conclusion, pRNFL and GCIPL thickness thresholds for single-time-point SS-OCT measurements may be helpful in differentiating the disability status of treatment-naïve pwMS.