How effective is eccentric viewing training? A systematic literature review

Purpose: The global prevalence of age-related macular degeneration (AMD) and associated central vision loss (CVL) is rising. CVL hinders the performance of many activities of daily living (ADLs). Adaptive strategies such as eccentric viewing (EV) and steady eye strategy (SES) may be used to compensate for CVL. In order to establish the potential of these rehabilitation strategies, this systematic review evaluates current literature regarding the effectiveness of EV and SES training in people with CVL. Recent Findings: The search strategies identified 2605 publications, 36 of which met the inclusion criteria for the review, but only three of which were randomised controlled trials. This literature shows that EV and SES training can improve near visual acuity, reading speed, and performance of ADLs in people with CVL. However, there was insufficient literature to establish a relationship between training and distance visual acuity or quality-of-life. There is no conclusive evidence to show that a particular model of EV training is superior to another, little clear evidence of a relationship between participant characteristics and training outcomes and no data regarding the cost effectiveness of training. Summary: This report highlights the need for further robust research to establish the true potential and cost effectiveness of EV and SES training as a rehabilitation strategy for individuals with CVL

Visual impairment is associated with physical and mental comorbidities in older adults:a cross-sectional study

Background<p></p> Visual impairment is common in older people and the presence of additional health conditions can compromise health and rehabilitation outcomes. A small number of studies have suggested that comorbities are common in visual impairment; however, those studies have relied on self-report and have assessed a relatively limited number of comorbid conditions.<p></p> Methods<p></p> We conducted a cross-sectional analysis of a dataset of 291,169 registered patients (65-years-old and over) within 314 primary care practices in Scotland, UK. Visual impairment was identified using Read Code ever recorded for blindness and/or low vision (within electronic medical records). Prevalence, odds ratios (from prevalence rates standardised by stratifying individuals by age groups (65 to 69 years; 70 to 74; 75 to 79; 80 to 84; and 85 and over), gender and deprivation quintiles) and 95% confidence intervals (95% CI) of 37 individual chronic physical/mental health conditions and total number of conditions were calculated and compared for those with visual impairment to those without.<p></p> Results<p></p> Twenty seven of the 29 physical health conditions and all eight mental health conditions were significantly more likely to be recorded for individuals with visual impairment compared to individuals without visual impairment, after standardising for age, gender and social deprivation. Individuals with visual impairment were also significantly more likely to have more comorbidities (for example, five or more conditions (odds ratio (OR) 2.05 95% CI 1.94 to 2.18)).<p></p> Conclusions<p></p> Patients aged 65 years and older with visual impairment have a broad range of physical and mental health comorbidities compared to those of the same age without visual impairment, and are more likely to have multiple comorbidities. This has important implications for clinical practice and for the future design of integrated services to meet the complex needs of patients with visual impairment, for example, embedding depression and hearing screening within eye care services

Dysregulation in Retinal Para-Inflammation and Age-Related Retinal Degeneration in CCL2 or CCR2 Deficient Mice

We have shown previously that a para-inflammatory response exists at the retinal/choroidal interface in the aging eye; and this response plays an important role in maintaining retinal homeostasis under chronic stress conditions. We hypothesized that dysregulation of the para-inflammatory response may result in an overt pro-inflammatory response inducing retinal degeneration. In this study, we examined this hypothesis in mice deficient in chemokine CCL2 or its cognate receptor CCR2. CCL2- or CCR2-deficient mice developed retinal degenerative changes with age, characterized as retinal pigment epithelial (RPE) cell and photoreceptor cell death. Retinal cell death was associated with significantly more subretinal microglial accumulation and increased complement activation. In addition, monocytes from CCL2- or CCR2-deficient mice had reduced capacity for phagocytosis and chemotaxis, expressed less IL-10 but more iNOS, IL-12 and TNF-α when compared to monocytes from WT mice. Complement activation at the site of RPE cell death resulted in C3b/C3d but not C5b-9 deposition, indicating only partial activation of the complement pathway. Our results suggest that altered monocyte functions may convert the protective para-inflammatory response into an overtly harmful inflammation at the retina/choroidal interface in CCL2- or CCR2-deficient mice, leading to RPE and photoreceptor degeneration. These data support a concept whereby a protective para-inflammatory response relies upon a normally functioning innate immune system. If the innate immune system is deficient chronic stress may tip the balance towards an overt inflammatory response causing cell/tissue damage

Neurodegeneration of the retina in mouse models of Alzheimer’s disease: what can we learn from the retina?

Alzheimer’s disease (AD) is an age-related progressive neurodegenerative disease commonly found among elderly. In addition to cognitive and behavioral deficits, vision abnormalities are prevalent in AD patients. Recent studies investigating retinal changes in AD double-transgenic mice have shown altered processing of amyloid precursor protein and accumulation of β-amyloid peptides in neurons of retinal ganglion cell layer (RGCL) and inner nuclear layer (INL). Apoptotic cells were also detected in the RGCL. Thus, the pathophysiological changes of retinas in AD patients are possibly resembled by AD transgenic models. The retina is a simple model of the brain in the sense that some pathological changes and therapeutic strategies from the retina may be observed or applicable to the brain. Furthermore, it is also possible to advance our understanding of pathological mechanisms in other retinal degenerative diseases. Therefore, studying AD-related retinal degeneration is a promising way for the investigation on (1) AD pathologies and therapies that would eventually benefit the brain and (2) cellular mechanisms in other retinal degenerations such as glaucoma and age-related macular degeneration. This review will highlight the efforts on retinal degenerative research using AD transgenic mouse models

Amd classification in choroidal oct using hierarchical texton mining

In this paper, we propose a multi-step textural feature extraction and classification method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, extracts spatial information using clustering and Local Binary Patterns (LBP) and then generalizes the discriminative power by forming a histogram based descriptor. It integrates the concept of hierarchical texton mining and data driven kernel learning into a uniform framework. The proposed method is applied to a practical medical diagnosis problem of classifying different stages of Age-Related Macular Degeneration (AMD) using a dataset comprising long-wavelength Optical Coherence Tomography (OCT) images of the choroid. The results demonstrate the feasibility of our method for classifying different AMD stages using the textural information of the choroidal region

Age-related macular degeneration detection and stage classification using choroidal oct images

Age-Related Macular Degeneration (AMD) is a progressive eye disease which damages the retina and causes visual impairment. Detecting those in the early stages at most risk of progression will allow more timely treatment and preserve sight. In this paper, we propose a machine learning based method to detect AMD and distinguish the different stages using choroidal images obtained from optical coherence tomography (OCT). We extract texture features using a Gabor filter bank and non-linear energy transformation. Then the histogram based feature descriptors are used to train the random forests, Support Vector Machine (SVM) and neural networks, which are tested on our choroid OCT image dataset with 21 participants. The experimental results show the feasibility of our method

Learning feature extractors for AMD classification in OCT using convolutional neural networks

In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. The proposed method is applied to a practical medical diagnosis problem of classifying different stages of Age-Related Macular Degeneration (AMD) using a dataset comprising long-wavelength Optical Coherence Tomography (OCT) images of the choroid. The experimental results show that the proposed method extracts more discriminative features than the features learnt through CNN only. It also suggests the feasibility of classifying different AMD stages using the textural information of the choroid region

ETDRS grid and example 10 intra-retinal layer segmentation.

<p>(A) Standard ETDRS grid showing the foveal subfield (black). The inner ring is an average of the four parafoveal subfields (dark grey) and the outer ring of the four perifoveal subfields (light grey). (B) Screenshot of 10 layer (11 boundary) segmentation of a long-wavelength OCT image, produced by the Iowa Reference Algorithms. The left half of the image shows the image prior to segmentation. Layers 1–10 (top to bottom; as defined by the software): retinal nerve fiber layer (RNFL); ganglion cell layer (GCL); inner plexiform layer (IPL); inner nuclear layer (INL); outer plexiform layer (OPL); outer plexiform layer-Henle fiber layer to boundary of myoid and ellipsoid of inner segments (OPL-HFL ~ BMEIS); photoreceptor inner/outer segments (IS/OS); inner/outer segment junction to inner boundary of outer segment photoreceptor/retinal pigment epithelium complex (IS/OSJ ~ IB_RPE); outer segment photoreceptor/retinal pigment epithelium complex (OPR); retinal pigment epithelium (RPE).</p