Optical Coherence Tomography in Chronic Relapsing Inflammatory Optic Neuropathy, Neuromyelitis Optica and Multiple Sclerosis: A Comparative Study

Eslami, Maziar; Lichtman-Mikol, Samuel; Razmjou, Sara; Bernitsas, Evanthia

doi:10.3390/brainsci12091140

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…In MOG-associated acute neuritis, the nerve fiber layer is thinner compared to that in acute neuritis with multiple sclerosis [30,31].…”

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confidence: 90%

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Optic Neuritis or Inflammatory Optic Neuropathy: a review

Moyseyenko¹

2022

Oftalmol Zh

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Optic neuritis [1] and inflammatory optic neuropathy [2] are two terms which can be found in the literature and which explain a large group of inflammations of the optic nerve. The discussion on the correct use of the terms and the understanding of the nature of damage requires more detailed research and literature search.Purpose. To study the current views on the pathogenesis of optic nerve inflammation.Methods. The literature search in Ukrainian and foreign scientific sources (32 sources).The classification of neuritis, theories of pathogenesis and approaches to their treatment in Ukrainian ophthalmology and in the world societies of neuroophthalmology (NANOS and EUNOS) are different. In Ukraine, the most commonly used classification is based on localization (papillitis and retrobulbar neuritis) [3], while abroad they prefer the classification based on pathogenesis (typical and atypical neuritis).Until recently, up to 2016, the difference between typical neuritis, which is considered a consequence of demyelinating diseases, and atypical neuritis, which has a different nature, was clearer (Table 1). [4].On the other hand [5], the diversity of manifestations of atypical forms, in which both papillitis and retrobulbar neuritis variants are possible (Fig. 1), shows, in our opinion, the imperfection of the Ukrainian neuritis classification system.

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“…In MOG-associated acute neuritis, the nerve fiber layer is thinner compared to that in acute neuritis with multiple sclerosis [30,31].…”

mentioning

confidence: 90%

“…

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confidence: 99%

Optic Neuritis or Inflammatory Optic Neuropathy: a review

Moyseyenko¹

2022

Oftalmol Zh

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“…The utilization of advanced machine learning methodologies, in conjunction with intricate feature extraction procedures and expansive, varied datasets, exhibits the potential to augment the precision and resilience of MS identi cation from scanning laser ophthalmoscopy pictures. Through ongoing re nement and optimization of these methodologies, scholars and medical practitioners have the ability to fully harness the potential of SLO as a helpful instrument in the timely identi cation and treatment of multiple sclerosis (MS), eventually resulting in enhanced patient outcomes and a more comprehensive comprehension of this intricate neurological condition [5][6][7]9]. In [10] conducted a meta-analysis of existing studies utilizing retinal imaging to assess vascular changes in MS patients, employing machine learning techniques for data synthesis and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the goal of precisely identifying multiple sclerosis using SLO pictures continues to pose di culties owing to the intricate nature of the image data and the substantial variations in retinal characteristics across diverse people. As a consequence, some methodologies may encounter constraints in terms of precision and responsiveness, impeding their capacity to attain consistent and dependable mass spectrometry detection outcomes [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Long Short-Term Memory (LSTM) for Detecting Multiple Sclerosis Based on Vessel Analysis

yaghoubi,

kafieh

2023

Preprint

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Background: Multiple Sclerosis (MS) is a chronic immune-mediated disease affecting the central nervous system, leading to various disturbances, including visual impairment. Early and accurate diagnosis of MS is critical for effective treatment and management. Scanning Laser Ophthalmoscopy (SLO) is a non-invasive technique that provides high-quality retinal images, serving as a promising resource for the early detection of MS. This research investigates a vessel-based approach for MS detection in SLO images using Long Short-Term Memory (LSTM) networks. Material and Methods: A total of 106 Healthy Controls (HCs) and 39 MS patients (78 eyes) were enrolled. After implementing quality control measures and removing poor-quality or damaged images, the research utilized a total of 265 photos (73 MS and 192 HC). An approach for the early detection of MS in SLO images using LSTM networks is introduced. This approach involves two steps: 1. Pre-training a deep neural network on the source dataset, and 2. Tuning the network on the target dataset of SLO images. Results: The significance of vessel segmentation in MS detection is examined, and the application of the proposed method in improving diagnostic models is explored. The proposed approach achieves an accuracy rate of 97.44% when evaluated on a test dataset consisting of SLO pictures. Conclusions: Through experiments on SLO datasets and employing the proposed vessel-based approach with LSTM, empirical results demonstrate that this approach contributes to the early detection of MS with high accuracy. These models exhibit the capability to accurately detect the disease with high precision and appropriate sensitivity.

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Retinal optical coherence tomography measures in multiple sclerosis: a systematic review and meta‐analysis

El Ayoubi,

Ismail,

Fahd

et al. 2024

Ann Clin Transl Neurol

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Spectral domain‐optical coherence tomography plays a crucial role in the early detection and monitoring of multiple sclerosis (MS) pathophysiology. We aimed to quantify differences in retinal layer measures among different groups of MS and explored different variables that correlate with retinal measures. This study was reported according PRISMA guidelines. A comprehensive search was done across PubMed, Embase, and Google Scholar. The mean difference in thickness of retinal layers and macular volume was assessed. Meta‐regression was done to assess the sources of heterogeneity. A total of 100 articles were included in the meta‐analyses. The peripapillary retinal nerve fiber layer (pRNFL) thickness significantly decreased in the MSON (MD: −16.44, P < 0.001), MSNON (MD: −6.97, P < 0.001), and PMS (MD: −11.35, P < 0.001) versus HC. The macular RNFL was lower among the MSON (MD: −6.24, P = 0.013) and MSNON (MD: −3.84, P <0.001) versus HC. Macular ganglion cell layer and inner plexiform layer (GCIPL) was thinner among MSON (MD: −14.83, P <0.001), MSNON (MD: −6.38, P < 0.001), and PMS (MD: −11.52, P < 0.001) compared with control eyes. Inner nuclear layer (INL) was higher in the MSON (MD: 0.49, P < 0.001) versus HC. Outer nuclear layer (ONL) thickness significantly lower in the MSNON (MD: −1.15, P = 0.019) versus HC. Meta‐regression showed that disease duration, age, EDSS score, and percentage of patients taking DMT are all negatively correlated with pRNFL and GCIPL thickness; however, female gender was correlated with less atrophy. As conclusion, the study highlights substantial thinning in the pRNFL and macular GCIPL between MS versus controls. INL as valuable parameter for capturing inflammatory disease activity.

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Optical Coherence Tomography in Chronic Relapsing Inflammatory Optic Neuropathy, Neuromyelitis Optica and Multiple Sclerosis: A Comparative Study

Cited by 10 publications

References 35 publications

Optic Neuritis or Inflammatory Optic Neuropathy: a review

Optic Neuritis or Inflammatory Optic Neuropathy: a review

Utilizing Long Short-Term Memory (LSTM) for Detecting Multiple Sclerosis Based on Vessel Analysis

Retinal optical coherence tomography measures in multiple sclerosis: a systematic review and meta‐analysis

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