Visual Discomfort Scores Research Articles

Temporal effects of an original myopia song on school children’s myopia and awareness: a 3-year prospective study

ABSTRACT Clinical Relevance Raising children’s myopia prevention awareness and behaviour tends to exhibits a protective effect against myopia among schoolchildren. Background To investigate the effect of an original myopia song in raising school children’s awareness of healthier eye use behaviour and on myopia prevention. Methods In this prospective randomised control study, two groups of students from one primary schools in Jiangxi, China, were enrolled and monitored from grade 3 to grade 6 (2016–2019). The primary outcome was the change in axial length (AL) after the intervention. Secondary outcomes included changes in spherical equivalent refraction (SER), near work and outdoor time, corrected near and distant visual acuity, visual discomfort score (VDS) and accommodative lag. Results Four hundred students (193 females, 48.25%) aged 9.3 (range 8–10) years with emmetropia to moderate myopia were enrolled. Children in the myopia song group had a significantly shorter AL and less myopic refractive change than those in the control group (p = 0.04 and 0.02, respectively). Compared with the control group, children in the myopia song group spent less time on near work and more time outdoors (p = 0.04 and 0.04, respectively). At the final follow-up, the proportion of children with myopia was significantly lower in the myopia song group (30.5%) than in the control group (41%) (p = 0.03). No significant differences were found for the secondary outcomes, including corrected near and distant visual acuity, VDS and accommodative lag. Conclusions An original myopia song performed twice daily in primary schools appeared to have a modest effect on myopia control among school-aged children by changing their lifestyles in the long term.

Bi-disparity sparse feature learning for 3D visual discomfort prediction

Viewing stereoscopic images sometimes causes viewers to feel inconvenience, which is called 3D visual discomfort. Excessive horizontal disparity, misalignment between the left and right views, or depth cues conflicts are some of the important factors involved in 3D visual discomfort. The ability to estimate the degree of 3D visual discomfort can be used to improve the 3D display systems and provide acceptable binocular visual quality. Most of the existing visual discomfort prediction (VDP) approaches extract hand-crafted features based on perceptual modeling and statistical analysis of disparities. We have proposed a simple yet effective VDP model based on unsupervised learning of sparse features which are highly predictive of subjective discomfort levels. These features are extracted from the aggregation of left and right disparity maps. This aggregation effectively highlights the areas with sudden changes and high levels of disparities where discomfort is most likely to occur. The regression model trained by the features, predicts high correlated 3D visual discomfort scores on each dataset. The cross-database results are also superior to other reported ones.

Reproducibility of the Dunlop Test for Motor Ocular Dominance Stability Among Young Saudi Females

Abstract Background: Dunlop test is reliable for assessing ocular dominance, however, its reproducibility never been tested. Aim of Study: To determine the reproducibility of the Dunlop test for determination of ocular dominant stability and the effects of visual fatigue among young Saudi female. Subjects and Methods: Fifty-three healthy females (range, 19-23 years) were assessed for sighting and motor ocular dominance using the hole-in-the-card test and Dunlop test, respectively. The Dunlop test was assessed twice in two different visits and was repeated the third time for a subgroup at the week's end to assess the effect of visual fatigue on stability of ocular dominance by using the visual discomfort scores (VDCS). Results: The mean (± standard deviation) logMAR visual acuity was similar for the right and left eyes. A significant proportion of the participants were right handed with right sighting eye dominance. There was no significant agreement between writing hand and sighting eye dominance; however, the Dunlop test results were in agreement with the hole-in-the-card test results. In total, 85% of the participants with right eye dominance on the Dunlop test had right sighting eye dominance. In total, 55.6% of those who had left motor eye dominance had left sighting eye dominance. Overall, 75.5% showed same eye dominance using both tests. Of the partici-pants who showed right eye dominance in visit 1, all but one participant (93%) showed the same eye dominance in visit 2, thus indicating a strong agreement between motor ocular dominance in the two visits. The mean VDCS was significantly greater in visit 3 than in visits 1 and 2. Conclusion: The sighting eye of female Saudi adults match their dominant hand in a majority of cases. Clinicians should be aware that the dominant eye for the same individual may differ depending on the test used. The stability of motor ocular dominance is unaffected by visual fatigue and the Dunlop test gives reproducible results for ocular dominance determination.

Effect of Induced Visual Fatigue on Sensory Ocular Dominance and the Effect of Colored Filters on the Levels of Visual Fatigue in Dyslexic Adults with MIS

The aim of the current research was to investigate the effect of visual fatigue induced with different clinical test stimuli on the sensory ocular dominance in dyslexic adults with Mearles-Irlen syndrome (MIS) and examine the role of colored filters. Nine female participants with dyslexia plus MIS aged between 18 and 36 years were recruited. Sensory ocular dominance and binocular vision functions were measured before and after induction of visual fatigue was induced. Measurements were conducted with participants wearing their habitual colored filters and without the filters and in two different sessions. Constant and continuous (static) test stimuli were used to create binocular rivalry. To induce visual fatigue, participants were asked to read, copy, and search for thirty minutes; the participants performed each tasks for ten minutes, while wearing their habitual near spectacle lens. No breaks were allowed between tasks. The calculated visual discomfort score (VDCS) for each test condition was analyzed. In sessions one and two respectively, VDCS was increased from 0.67 ± 1.00 to 1.44 ± 0.53 (<i>p</i> = 0.07) and 0.22 ± 0.44 to 1.56 ± 0.73 (<i>p</i> = 0.001), in adults with dyslexia and MIS after assessment of binocular vision function. Similarly, the VDCS was significantly increased to 2.56 ± 0.73 (<i>p</i> = 0.002) in session one and 2.11 ± 0.93 (<i>p</i> = 0.001) in session two, after visual fatigue was induced in these participants. There were no differences between both eyes’ sensory ocular dominance in dyslexic adults with MIS irrespective of the test stimuli used to induce the binocular rivalry. Induction of visual fatigue was followed by disruptions in the sensory ocular dominance that persisted with participants wearing their habitual colored overlays during the fatigue duration. This finding was also not influenced by the test slides used in these participants. Optometric investigations such as binocular vision testing, performed in dyslexic adults with MIS using certain stimuli could induce visual discomfort, but the discomfort induced by stressful visual testing was significantly greater. Moreover, the use of certain test stimuli was shown to disrupt the sensory ocular dominance of this group of participants suggesting that researchers should consider using the appropriate stimuli when examining patients with dyslexia and/or MIS and those experiencing reading difficulties.

A Risk-Aware Pairwise Rank Learning Approach for Visual Discomfort Prediction of Stereoscopic 3D

For visual discomfort prediction (VDP) of stereoscopic 3D images, a common two-stage framework is to first extract features that are predictive of the experienced visual discomfort level when viewing stereoscopic images and then use typical regression tools to learn the mapping from the extracted features to visual discomfort scores. Most existing approaches for stereoscopic 3D VDP focus on the former stage, i.e., feature extraction, while limited efforts have dedicated to exploiting more powerful and robust learning algorithms in this field. In this letter, inspired by the pairwise comparison-based subjective evaluation methodology, we propose a novel Risk-Aware Pairwise Rank Learning (RAPRL) approach to further improve the prediction accuracy. Unlike the traditional VDP approaches using different regression tools for feature-score mapping, our proposed RARL method addresses this problem based on a completely different pairwise rank learning framework with a risk-aware constraint. Experiments have verified the effectiveness and robustness of our proposed VDP model using RAPRL as the learning algorithm.

Learning based no-reference metric for assessing quality of experience of stereoscopic images

Human’s perception plays a very important role on image assessment, especially for stereoscopic images. In general, viewing stereoscopic 3D images will cause visual fatigue, eyestrain, dizziness or headache. Therefore, how to evaluate human’s perception of visual quality on 3D images becomes an emerging topic. In this paper, we propose a no-reference assessment metric for stereoscopic image quality of experience (QoE). First, the stereoscopic image pairs are used to calculate the disparity maps by optical flow estimation. Then the depth information are extracted from the disparity map, called as disparity-depth map. Next, we extract four types of features based on pixel value and distribution of disparity-depth map. Two regression models are used to predict visual discomfort scores. Also, we test the proposed method on EPFL 3D image database and IEEE-SA stereoscopic image database, respectively. The experiment results show that our proposed QoE assessment metric achieves excellent performance compared with state-of-the-art methods.

Deep Visual Discomfort Predictor for Stereoscopic 3D Images.

Most prior approaches to the problem of stereoscopic 3D (S3D) visual discomfort prediction (VDP) have focused on the extraction of perceptually meaningful handcrafted features based on models of visual perception and of natural depth statistics. Towards advancing performance on this problem, we have developed a deep learning based VDP model named Deep Visual Discomfort Predictor (DeepVDP). DeepVDP uses a convolutional neural network (CNN) to learn features that are highly predictive of experienced visual discomfort. Since a large amount of reference data is needed to train a CNN, we develop a systematic way of dividing S3D image into local regions defined as patches, and model a patch-based CNN using two sequential training steps. Since it is very difficult to obtain human opinions on each patch, instead a proxy ground-truth label that is generated by an existing S3D visual discomfort prediction algorithm called 3D-VDP is assigned to each patch. These proxy ground-truth labels are used to conduct the first stage of training the CNN. In the second stage, the automatically learned local abstractions are aggregated into global features via a feature aggregation layer. The learned features are iteratively updated via supervised learning on subjective 3D discomfort scores, which serve as ground-truth labels on each S3D image. The patchbased CNN model that has been pretrained on proxy groundtruth labels is subsequently retrained on true global subjective scores. The global S3D visual discomfort scores predicted by the trained DeepVDP model achieve state-of-the-art performance as compared to previous VDP algorithms.

3D visual discomfort predictor based on subjective perceived-constraint sparse representation in 3D display system

Three-dimensional (3D) display systems have been widely adopted due to the recent increased availability of an increasing 3D contents. However, viewers may experience visual discomfort due to the limited viewing zone available of 3D display systems. Therefore, 3D visual discomfort prediction is important for optimizing 3D display systems. In this paper, we propose a 3D visual discomfort predictor (3D-VDP) that is based on the visual discomfort features of the primary visual cortex (V1) and the properties of subjective perceived-constraint sparse representation (SPCSR). Embedding subjective values of visual discomfort as a constraint into sparse representation such that the dictionary is more suitable for visual perception is the major technical contribution of this study. Specifically, the proposed 3D-VDP with SPCSR consists of two phases. In the training phase, first, the neural activity mechanism of V1 is considered, and the features of visually important disparity and spatial frequency disparity are extracted to highlight the influence of disparity on the comfort of stereoscopic images. Second, by considering the visual properties of the receptive field and learning mechanism, a perceived dictionary of visual discomfort and the corresponding perceived value of visual discomfort are obtained based on the subjective value of visual discomfort as a constraint condition applied to the construction of a supervised dictionary learning algorithm. In the testing phase, the sparse coefficient of visual discomfort of the stereoscopic image is computed according to the perceived dictionary of visual discomfort by using the sparse coding algorithm, and the final visual discomfort score of the stereoscopic image is obtained from the weighted sparse coefficients and the perceived value of visual discomfort. Experimental results obtained with the IVY LAB database and the NBU database demonstrate that, in comparison with closely related existing models, the proposed 3D-VDP with SPCSR achieves a high consistency of subjective assessment.

Using electromyography responses to investigate the effects of the display type, viewing distance, and viewing time on visual fatigue

The rapid success of 3D display technology and daily accessibility to 3D images has greatly increased the interest in such applications for a wide range of fields. This paper compares the effects of watching movies with 2D and 3D displays depending on the viewing distance (3Hvs. 6H, where H is the height of the screen) and viewing time to determine the visual fatigue using electromyography (EMG) in terms of the percentage of maximum voluntary contraction (%MVC) of the orbicularis oculi (OO) muscle activity and a subjective visual discomfort score. Twenty healthy male university students with a mean age of 27.7±2.53years participated in this study as volunteers. None had color blindness, and all had normal vision acuity. A mixed-measures design was performed. The results showed that the viewing time and distance had significant effects on the %MVC and OO muscle activity depending on the display type. Watching the 3D display from a short viewing distance produced significantly high visual fatigue compared to watching the 2D display from a short viewing distance. However, the 3D display seemed to be less stressful than the 2D display at long viewing distances.

Transfer Function Model of Physiological Mechanisms Underlying Temporal Visual Discomfort Experienced When Viewing Stereoscopic 3D Images.

When viewing 3D images, a sense of visual comfort (or lack of) is developed in the brain over time as a function of binocular disparity and other 3D factors. We have developed a unique temporal visual discomfort model (TVDM) that we use to automatically predict the degree of discomfort felt when viewing stereoscopic 3D (S3D) images. This model is based on physiological mechanisms. In particular, TVDM is defined as a second-order system capturing relevant neuronal elements of the visual pathway from the eyes and through the brain. The experimental results demonstrate that the TVDM transfer function model produces predictions that correlate highly with the subjective visual discomfort scores contained in the large public databases. The transfer function analysis also yields insights into the perceptual processes that yield a stable S3D image.

3D visual discomfort predictor: analysis of horizontal disparity and neural activity statistics.

Being able to predict the degree of visual discomfort that is felt when viewing stereoscopic 3D (S3D) images is an important goal toward ameliorating causative factors, such as excessive horizontal disparity, misalignments or mismatches between the left and right views of stereo pairs, or conflicts between different depth cues. Ideally, such a model should account for such factors as capture and viewing geometries, the distribution of disparities, and the responses of visual neurons. When viewing modern 3D displays, visual discomfort is caused primarily by changes in binocular vergence while accommodation in held fixed at the viewing distance to a flat 3D screen. This results in unnatural mismatches between ocular fixations and ocular focus that does not occur in normal direct 3D viewing. This accommodation vergence conflict can cause adverse effects, such as headaches, fatigue, eye strain, and reduced visual ability. Binocular vision is ultimately realized by means of neural mechanisms that subserve the sensorimotor control of eye movements. Realizing that the neuronal responses are directly implicated in both the control and experience of 3D perception, we have developed a model-based neuronal and statistical framework called the 3D visual discomfort predictor (3D-VDP)that automatically predicts the level of visual discomfort that is experienced when viewing S3D images. 3D-VDP extracts two types of features: 1) coarse features derived from the statistics of binocular disparities and 2) fine features derived by estimating the neural activity associated with the processing of horizontal disparities. In particular, we deploy a model of horizontal disparity processing in the extrastriate middle temporal region of occipital lobe. We compare the performance of 3D-VDP with other recent discomfort prediction algorithms with respect to correlation against recorded subjective visual discomfort scores,and show that 3D-VDP is statistically superior to the other methods.

Measurement of visual sensitivity in migraine: Validation of two scales and correlation with visual cortex activation.

The objectives of this article are to compare interictal and ictal visual sensitivity between migraine and controls using two published questionnaires, and to correlate responses with a physiologic measure of visual cortex activation. Migraine with (MWA, n = 51) and without (MwoA, n = 45) aura and control individuals (n = 45) were enrolled and underwent BOLD fMRI with a visual stimulus. The visual discomfort score (VDS) assessed interictal and the migraine photophobia score (MPS) assessed ictal visual sensitivity. VDS was significantly higher both in MWA and MwoA vs controls (both p = 0.0001). MPS was greater in MWA vs MwoA (p = 0.008). Ictal and interictal visual sensitivity strongly correlated in MWA (p = 0.004) but not MwoA patients (p = 0.12). BOLD activation in visual cortex was greater in MWA vs controls (2.7% vs 2.3%, p = 0.003) but similar between MwoA and controls. Increasing VDS was associated with greater BOLD signal change in MWA (p = 0.03) but not MwoA (p = 0.65) or controls (p = 0.53). MPS did not correlate with BOLD activation in either group. Increased interictal visual sensitivity is present both in MWA and MwoA. However, the correlation with ictal visual sensitivity and with cortical hyper-responsivity varies between MWA and MwoA, suggesting underlying differences between groups.

Laparoscopic versus Open Approach for Aortobifemoral Bypass for Severe Aorto-iliac Occlusive Disease – A Multicentre Randomised Controlled Trial

ObjectivesTo investigate differences between open and laparoscopic aortobifemoral bypass surgery for aorto-iliac occlusive disease on postoperative morbidity and mortality. DesignA multicentre randomised controlled trial. MethodsBetween January 2007 and November 2009, 28 patients with severe aorto-iliac occlusive disease (TASC II C or D) were randomised between laparoscopic and open approach at one community hospital and one university hospital (TASC = Trans-Atlantic Inter-Society Consensus on the Management of Peripheral Arterial Disease). ResultsThe operation time was longer for the laparoscopic approach (mean 4 h 19 min (2 h 00 min to 6 h 20 min) vs. 3 h 30 min (1 h 42 min to 5 h 11 min); p = 0.101)). Nevertheless, postoperative recovery and in-hospital stay were significantly shorter after laparoscopic surgery. Also oral intake could be restarted earlier (mean 20 h 34 min (6 h 00 min to 26 h 55 min) vs. 43 h 43 min (19 h 40 min to 77 h 30 min); p = 0.00014)) as well as postoperative mobilisation (walking) (mean 46 h 15 min (16 h 07 min to 112 h 40 min) vs. mean 94 h 14 min (66 h 10 min to 127 h 23 min); p = 0.00016)). Length of hospitalisation was shorter (mean 5.5 days (2.5–15) vs. mean 13.0 days (7–45); p = 0.0095)). Visual pain scores and visual discomfort scores were both lower after laparoscopic surgery. Also return to normal daily activities was achieved earlier. There were no major complications in both groups. ConclusionLaparoscopic aortobifemoral bypass surgery for aorto-iliac occlusive disease is a safe procedure with a significant decrease in postoperative morbidity and in-hospital stay and earlier recovery.