Investigating Multimodal Diagnostic Eye Biomarkers of Cognitive Impairment by Measuring Vascular and Neurogenic Changes in the Retina

Previous studies have demonstrated that cognitive impairment (CI) is not limited to the brain but also affects the retina. In this pilot study, we investigated the correlation between the retinal vascular complexity and neurodegenerative changes in patients with CI using a low-cost multimodal approach. Quantification of the retinal structure and function were conducted for every subject (n = 69) using advanced retinal imaging, full-field electroretinogram (ERG) and visual performance exams. The retinal vascular parameters were calculated using the Singapore Institute Vessel Assessment software. The Montreal Cognitive Assessment was used to measure CI. Pearson product moment correlation was performed between variables. Of the 69 participants, 32 had CI (46%). We found significantly altered microvascular network in individuals with CI (larger venular-asymmetry factor: 0.7 ± 0.2) compared with controls (0.6 ± 0.2). The vascular fractal dimension was lower in individuals with CI (capacity, information and correlation dimensions: D0, D1, and D2 (mean ± SD): 1.57 ± 0.06; 1.56 ± 0.06; 1.55 ± 0.06; age 81 ± 6years) vs. controls (1.61 ± 0.03; 1.59 ± 0.03; 1.58 ± 0.03; age: 80 ± 7 years). Also, drusen-like regions in the peripheral retina along with pigment dispersion were noted in subjects with mild CI. Functional loss in color vision as well as smaller ERG amplitudes and larger peak times were observed in the subjects with CI. Pearson product moment correlation showed significant associations between the vascular parameters (artery-vein ratio, total length-diameter ratio, D0, D1, D2 and the implicit time (IT) of the flicker response but these associations were not significant in the partial correlations. This study illustrates that there are multimodal retinal markers that may be sensitive to CI decline, and adds to the evidence that there is a statistical trend pointing to the correlation between retinal neuronal dysfunction and microvasculature changes suggesting that retinal geometric vascular and functional parameters might be associated with physiological changes in the retina due to CI. We suspect our analysis of combined structural-functional parameters, instead of individual biomarkers, may provide a useful clinical marker of CI that could also provide increased sensitivity and specificity for the differential diagnosis of CI. However, because of our study sample was small, the full extent of clinical applicability of our approach is provocative and still to be determined

The Role of Intravitreal Corticosteroids in the Treatment of DME: Predictive OCT Biomarkers

Abstract: This work aims to summarize predictive biomarkers to guide treatment choice in DME. Intravitreal anti-VEGF is considered the gold standard treatment for centers involving DME, while intravitreal steroid treatment has been established as a second-line treatment in DME. However, more than 1/3 of the patients do not adequately respond to anti-VEGF treatment despite up to 4-weekly injections. Not surprisingly, insufficient response to anti-VEGF therapy has been linked to low-normal VEGF levels in the serum and aqueous humor. These patients may well benefit from an early switch to intravitreal steroid treatment. In these patients, morphological biomarkers visible in OCT may predict treatment response and guide treatment decisions. Namely, the presence of a large amount of retinal and choroidal hyperreflective foci, disruption of the outer retinal layers and other signs of chronicity such as intraretinal cysts extending into the outer retina and a lower choroidal vascular index are all signs suggestive of a favorable treatment response of steroids compared to anti-VEGF. This paper summarizes predictive biomarkers in DME in order to assist individual treatment decisions in DME. These markers will help to identify DME patients who may benefit from primary dexamethasone treatment or an early switc

The Use of Optical Coherence Tomography for the Detection of Early Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the leading causes of vision loss globally with a severe burden on all societies due to its high treatment and rehabilitation costs. The early diagnosis of DR may provide preventive steps (including retinal laser therapy and tight carbohydrate, blood pressure, and cholesterol control) that could in turn help to avoid progression of the pathology with the resultant vision loss. Optical coherence tomography (OCT) enables the in vivo structural imaging of the retina, providing both qualitative (structure) and quantitative (thickness) information. In the past decades, extensive OCT research has been done in the field of DR. In the present review, we are focusing on those that were aiming at detection of the earliest retinal changes before DR could be diagnosed funduscopically. The latest, widely available technology of spectral-domain (SD-)OCT comes with a fast and reliable retinal imaging, which, together with the most recent developments in image processing and artificial intelligence, holds the promise of developing a quick and efficient, state-of-the-art screening tool for DR

Inter-session repeatability of retinal layer thickness in optical coherence tomography

Reliable retinal layer thickness measurements using optical coherence tomography (OCT) are important to track the subtle retinal changes in longitudinal studies. A total of 10 eyes (5 healthy subjects, 40±13 years old) were enrolled to study the inter-session repeatability and identify the pitfalls affecting the reliabilities. Each eye was scanned using spectral domain OCT (Spectralis SDOCT, Heidelberg Engineering) for 3 sessions with 30 seconds rest in between. The first and second sessions were scanned independently and the third one was scanned with the first one as the baseline visit. Each session consisted of a confocal scanning laser ophthalmoscopy (cSLO) image and 61 B-scans of 496×768 pixels. The first, second and third sessions were named as baseline, unregistered and registered sessions; respectively. Seven retinal layers labeled as RNFL, GCL+IPL, INL, OPL, ONL, IS and OS were segmented using a custom software (OCTRIMA3D) and measured in the ETDRS grid. Inter-session standard deviation (σ), coefficient of repeatability (CR) and coefficient of variations (COV) were calculated to quantify the repeatability. Paired t-test of COVs was used to compare the repeatability and the level of significance was set at 5%. We obtained that values of the CR <5 μm and COV of 5%, were revealed only in the outer layers. The values of COV were not significantly different (p<0.05) in the unregistered scanning session. Our results show that the rotations in the unregistered scanning sessions do not cause significant change in repeatability

The assessment of fundus image quality labeling reliability among graders with different backgrounds

PurposeFor the training of machine learning (ML) algorithms, correctly labeled ground truth data are inevitable. In this pilot study, we assessed the performance of graders with different backgrounds in the labeling of retinal fundus image quality. MethodsColor fundus photographs were labeled using a Python-based tool using four image categories: excellent (E), good (G), adequate (A) and insufficient for grading (I). We enrolled 8 subjects (4 with and 4 without medical background, groups M and NM, respectively) to whom a tutorial was presented on image quality requirements. We randomly selected 200 images from a pool of 18,145 expert-labeled images (50/E, 50/G, 50/A, 50/I). The performance of the grading was timed and the agreement was assessed. An additional grading round was performed with 14 labels for a more objective analysis. ResultsThe median time (interquartile range) for the labeling task with 4 categories was 987.8 sec (418.6) for all graders and 872.9 sec (621.0) vs. 1019.8 sec (479.5) in the M vs. NM groups, respectively. Cohen's weighted kappa showed moderate agreement (0.564) when using four categories that increased to substantial (0.637) when using only three by merging the E and G groups. By the use of 14 labels, the weighted kappa values were 0.594 and 0.667 when assigning four or three categories, respectively. ConclusionImage grading with a Python-based tool seems to be a simple yet possibly efficient solution for the labeling of fundus images according to image quality that does not necessarily require medical background. Such grading can be subject to variability but could still effectively serve the robust identification of images with insufficient quality. This emphasizes the opportunity for the democratization of ML-applications among persons with both medical and non-medical background. However, simplicity of the grading system is key to successful categorization