Exploring Retinal Blood Vessel Diameters as Biomarkers in Multiple Sclerosis

Nes, Dragana Drobnjak; Berg‐Hansen, Pål; Benavent, Sigrid A. de Rodez; Høgestøl, Einar August; Beyer, Mona K.; Rinker, Daniel A.; Veiby, Nina; Karabeg, Mia; Petrovski, Beáta Éva; Celius, Elisabeth Gulowsen; Harbo, Hanne F.; Petrovski, Goran

doi:10.3390/jcm11113109

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…The extent of retinal vascular pathology was also shown in a recent Norwegian study where 23 newly diagnosed and untreated pwMS had significantly smaller retinal venular and arteriolar total diameter when compared to matched 23 HCs. [19] While these anatomical changes did not influence the oxygen saturation, these pwMS reported significantly worse low-contrast acuity performance [19]. A recent and much larger OCT-A study of 111 pwMS suggested that lower density only in the superficial retinal vascular plexus was associated with MS-based disability scores [20].…”

Section: Discussionmentioning

confidence: 97%

Retinal Blood Vessel Analysis Using Optical Coherence Tomography (OCT) in Multiple Sclerosis

et al. 2023

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Background: Both greater retinal neurodegenerative pathology and greater cardiovascular burden are seen in people with multiple sclerosis (pwMS). Studies also describe multiple extracranial and intracranial vascular changes in pwMS. However, there have been few studies examining the neuroretinal vasculature in MS. Our aim is to determine differences in retinal vasculature between pwMS and healthy controls (HCs) and to determine the relationship between retinal nerve fiber layer (RNFL) thickness and retinal vasculature characteristics. Methods: A total of 167 pwMS and 48 HCs were scanned using optical coherence tomography (OCT). Earlier OCT scans were available for 101 pwMS and 35 HCs for an additional longitudinal analysis. Segmentation of retinal vasculature was performed in a blinded manner in MATLAB’s optical coherence tomography segmentation and evaluation GUI (OCTSEG) software. Results: PwMS has fewer retinal blood vessels when compared to HCs (35.1 vs. 36.8, p = 0.017). Over the 5.4 year follow up, and when compared to HCs, pwMS has a significant decrease in number of retinal vessels (average loss of −3.7 p = 0.007). Moreover, the total vessel diameter in pwMS does not change when compared to the increase in vessel diameter in the HCs (0.06 vs. 0.3, p = 0.017). Only in pwMS is there an association between lower RNFL thickness and fewer retinal vessel number and smaller diameter (r = 0.191, p = 0.018 and r = 0.216, p = 0.007). Conclusions: Over 5 years, pwMS exhibit significant retinal vascular changes that are related to greater atrophy of the retinal layers.

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Section: Discussionmentioning

confidence: 97%

Retinal Blood Vessel Analysis Using Optical Coherence Tomography (OCT) in Multiple Sclerosis

et al. 2023

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“…In addition to OCT, other retinal imaging modalities have also been employed to study MS. For instance, fundus camera photography has revealed optic nerve atrophy [7][8][9] and a decline in retinal vessel diameter [10,11] in patients with MS. Disruption of oxygen metabolism by retinal tissue has also been demonstrated using retinal oximetry, which is a technology based on the conventional fundus photography [10,12]. Similarly, OCT angiography (OCT-A) studies showed that vessel density (VD) of superior capillary plexus (SCP) within parafoveal and peripapillary regions decreases in eyes positive for optic neuritis [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Analyzing morphological alternations of vessels in multiple Sclerosis using SLO images of the eyes

Soltanipour,

Arian,

Aghababaei

et al. 2023

Preprint

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BackgroundOur purpose was to investigate the most relevant and discriminating clinical feature set of Scanning laser ophthalmoscopy (SLO) images, which could differentiate multiple sclerosis (MS) and healthy control (HC) patients.MethodsIn this work, SLO images were used for the first time to measure the most valuable manual and clinical features from some retinal structures, optic disc, cup and blood vessels, for MS and HC classifications. For this, first an age-matching algorithm along with a subject-wise k-fold cross-validation data splitting approach were applied for construction of training, validation and test dataset, minimizing the risk of model overestimation. Then, it was needed to segment the retinal structures from the SLO images, and due to the lack of ground truth for our SLO images, we took advantage of a previously proposed deep learning algorithm for anatomical segmentation using color fundus images. But owing to different imaging modalities of SLO images, we also used two stages of pre-processing and post-processing to obtain accurate results for the segmentation step. Following that, a set of manual and clinical features was measured from the segmented optic disc, cup and vessels to gain a better comprehension of the features playing an important role in classification of MS and HC images. Finally, three simple machine learning models were applied to evaluate the measured features and the most valuable and effective features were computed.ResultsThe measured feature set from the segmented optic disc, cup and blood vessels resulted in a mean accuracy (ACC) of 83%, sensitivity (SE) of 79%, specificity (SP) of 85%, and AUROC of 84%, when testing on validation data by using a XGBoost classifier model. Furthermore, horizontally disc location, fractal dimension and intensity variation of blood vessels were selected as the most important and effective features for MS and HC classification.ConclusionThe location of optic disc, fractal dimension and vessel intensity, the ratio between intensity of vessels to intensity of he whole SLO image, were selected as three most valuable features for MS and HC classification. Regarding the optic disc location, we found out the used SLO images had been captured with two different imaging techniques. So, this feature could not be trusted as the most important feature. Two other features were confirmed by one expert as clinically distinguishing features for MS and HC classification.

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“…This aspect is of great clinical relevance, especially when considering that SO-related complications can be severe and potentially sight-threatening [16]. Furthermore, imaging techniques allow the identification of new biomarkers with different potential applications, including the detection and prediction of progression or responses to the treatment of common ocular diseases (e.g., age-related macular degeneration, AMD, diabetic retinopathy, DR, and myopic choroidal neovascularization) [17][18][19]; the early detection of systemic diseases, including hypertension [20] and multiple sclerosis [21]; the prediction of functional outcomes after surgical procedures [22]; or the detection of potential complications associated with systemic dugs [23].…”

mentioning

confidence: 99%