Evaluation of retinal nerve fiber layer thickness measured by optical coherence tomography in Moroccan patients with multiple sclerosis

Soufi, G.; Aitbenhaddou, Elhachmia; Hajji, Z.; Tazrout, S.; Benomar, Ali; Soufi, M.; Boulanouar, A.; Abouqal, Rédouane; Yahyaoui, M.; Berraho, A.

doi:10.1016/j.jfo.2014.11.008

Cited by 11 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is in agreement with other studies Zamzam et al (14) , Khalil et al (15) and Singh et al (16) . Also, Soufi et al (17) evaluated retinal nerve fiber layer thickness measured by optical coherence tomography in patients with multiple sclerosis. Total RNFL thicknesses were significantly lower in MS eyes compared to healthy control eyes.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Retinal Nerve Fiber Layer Thickness in Patients with Multiple Sclerosis

Fahmi¹,

Kamel²,

Salem³

et al. 2021

The Egyptian Journal of Hospital Medicine

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Evaluation of Retinal Nerve Fiber Layer Thickness in Patients with Multiple Sclerosis

Fahmi¹,

Kamel²,

Salem³

et al. 2021

The Egyptian Journal of Hospital Medicine

View full text Add to dashboard Cite

“…172 RNFL thickness is reduced in ON and MS patients compared with healthy controls (Figure 2). This is seen using OCT in eyes with prior ON, 171,[173][174][175][176][177][178][179][180][181][182][183][184][185][186][187] particularly temporally and inferiorly. 176,177,188 The majority of studies also identified reduced pRNFL thickness in MS patients without prior history of ON (MS-NON), although to a lesser extent.…”

Section: Oct In Multiple Sclerosismentioning

confidence: 92%

Retinal correlates of neurological disorders

Yap

Si²,

et al. 2019

Therapeutic Advances in Chronic Disease

View full text Add to dashboard Cite

Considering the retina as an extension of the brain provides a platform from which to study diseases of the nervous system. Taking advantage of the clear optical media of the eye and ever-increasing resolution of modern imaging techniques, retinal morphology can now be visualized at a cellular level in vivo. This has provided a multitude of possible biomarkers and investigative surrogates that may be used to identify, monitor and study diseases until now limited to the brain. In many neurodegenerative conditions, early diagnosis is often very challenging due to the lack of tests with high sensitivity and specificity, but, once made, opens the door to patients accessing the correct treatment that can potentially improve functional outcomes. Using retinal biomarkers in vivo as an additional diagnostic tool may help overcome the need for invasive tests and histological specimens, and offers the opportunity to longitudinally monitor individuals over time. This review aims to summarise retinal biomarkers associated with a range of neurological conditions including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and prion diseases from a clinical perspective. By comparing their similarities and differences according to primary pathological processes, we hope to show how retinal correlates can aid clinical decisions, and accelerate the study of this rapidly developing area of research.

show abstract

“…Secondly, a study conducted in [11] assessed the thickness of the RNFL in individuals suffering from MS by using OCT scans. The study included 31 individuals with confirmed MS and 31 disease-free subjects.…”

Section: Cross-sectional / Thickness Observationsmentioning

confidence: 99%

An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

Irfanuddin,

Shafin,

Ahmed

et al. 2023

2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

View full text Add to dashboard Cite

Neurodegenerative disorders are diagnosed through undergoing brain MRI, CT scans, genetic testing, and various laboratory screening tests which are often tedious, timeconsuming and beyond the means of most people's financial capabilities and sometimes health unconducive. To remedy this, we proposed an efficient deep learning approach to detect neurodegenerative diseases, for instance, Multiple Sclerosis, Parkinson's disease, Amyotrophic Lateral Sclerosis, and Alzheimer's disease using retinal images. Efficient convolutional neural network-based architectures are used to classify brain diseases. The system enables the detection of brain diseases from retinal images rather than brain images effectively. Through the proposed system, we are able to proactively detect such disorders simply through retinal scans which are faster and simpler compared to the scanning of the brain itself which requires expensive and sophisticated equipment. We conducted our research on a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals. Our model achieved 100% accuracy in classifying all healthy and diseased individuals from retinal scans.

show abstract

Evaluation of retinal nerve fiber layer thickness measured by optical coherence tomography in Moroccan patients with multiple sclerosis

Cited by 11 publications

References 32 publications

Evaluation of Retinal Nerve Fiber Layer Thickness in Patients with Multiple Sclerosis

Evaluation of Retinal Nerve Fiber Layer Thickness in Patients with Multiple Sclerosis

Retinal correlates of neurological disorders

An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

Contact Info

Product

Resources

About