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"Blindheit."
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Prevalence of primary open angle glaucoma in the last 20 years: a meta-analysis and systematic review
Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness in the world and is influenced by various sociodemographic factors. This meta-analysis aims to determine the worldwide prevalence of POAG in the adult general population for the last 20 years, and explore variation in prevalence by age, gender and geographical location. An electronic literature search was performed using the PubMed, Embase, and Web of Science databases. Population-based cross-sectional or cohort studies published in the last 20 years (2000–2020) that reported prevalence of POAG were recruited. Relevant studies meeting defined eligibility criteria were selected and reviewed systematically by meta-analysis. The prevalence of POAG was analyzed according to various risk factors. A random effect model was used for the meta-analysis. Fifty publications with a total of 198,259 subjects were included in this meta-analysis. The worldwide overall prevalence of POAG was 2.4% (95% CI 2.0 ~ 2.8%). The prevalence increases with age. Men are found to be more susceptible to POAG than women (RR 1.28, p < 0.01). Africa is found to have the highest prevalence of POAG (4.0%) among all continents. The current estimated global population of POAG is 68.56 million (95% CI 59.99 ~ 79.98). POAG is a worldwide vision threatening disease with high prevalence for the last 20 years. The population-based prevalence of POAG varies widely across individual studies, due to variations in risk factors of age, gender, and population geographic location.
Journal Article
Detection and description generation of diabetic retinopathy using convolutional neural network and long short-term memory
Diabetic Retinopathy (DR) is one of the eye diseases suffered by diabetes patients that will cause blindness if it does not get effectively treated for a certain period of time. Early detection is needed to help patients get effective treatment based on their severity. Researchers have done copious amounts of research regarding the methods for DR detection using shallow learning and deep learning approaches. The proposed method in this paper is a combination of two deep learning architectures, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN is used to detect lesions on retinal fundus images, and LSTM is used for generating description sentences based on those lesions. In the training and testing process, the CNN output will be used for the input of LSTM. The training process's target is to produce a model that can map retinal fundus images into a sentence. The results of this experiment using the MESSIDOR data set has an accuracy of around 90%.
Journal Article
A combined convolutional and recurrent neural network for enhanced glaucoma detection
Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.
Journal Article
Cell Atlas of The Human Fovea and Peripheral Retina
Most irreversible blindness results from retinal disease. To advance our understanding of the etiology of blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of ~85,000 cells from the fovea and peripheral retina of seven adult human donors. Utilizing computational methods, we identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, retinal ganglion and non-neuronal cells. Nearly all types are shared between the two retinal regions, but there are notable differences in gene expression and proportions between foveal and peripheral cohorts of shared types. We then used the human retinal atlas to map expression of 636 genes implicated as causes of or risk factors for blinding diseases. Many are expressed in striking cell class-, type-, or region-specific patterns. Finally, we compared gene expression signatures of cell types between human and the cynomolgus macaque monkey,
Macaca fascicularis
. We show that over 90% of human types correspond transcriptomically to those previously identified in macaque, and that expression of disease-related genes is largely conserved between the two species. These results validate the use of the macaque for modeling blinding disease, and provide a foundation for investigating molecular mechanisms underlying visual processing.
Journal Article
Research on path planning of mobile robot based on improved ant colony algorithm
To solve the problems of local optimum, slow convergence speed and low search efficiency in ant colony algorithm, an improved ant colony optimization algorithm is proposed. The unequal allocation initial pheromone is constructed to avoid the blindness search at early planning. A pseudo-random state transition rule is used to select path, the state transition probability is calculated according to the current optimal solution and the number of iterations, and the proportion of determined or random selections is adjusted adaptively. The optimal solution and the worst solution are introduced to improve the global pheromone updating method. Dynamic punishment method is introduced to solve the problem of deadlock. Compared with other ant colony algorithms in different robot mobile simulation environments, the results showed that the global optimal search ability and the convergence speed have been improved greatly and the number of lost ants is less than one-third of others. It is verified the effectiveness and superiority of the improved ant colony algorithm.
Journal Article
Subretinally injected semiconducting polymer nanoparticles rescue vision in a rat model of retinal dystrophy
Inherited retinal dystrophies and late-stage age-related macular degeneration, for which treatments remain limited, are among the most prevalent causes of legal blindness. Retinal prostheses have been developed to stimulate the inner retinal network; however, lack of sensitivity and resolution, and the need for wiring or external cameras, have limited their application. Here we show that conjugated polymer nanoparticles (P3HT NPs) mediate light-evoked stimulation of retinal neurons and persistently rescue visual functions when subretinally injected in a rat model of retinitis pigmentosa. P3HT NPs spread out over the entire subretinal space and promote light-dependent activation of spared inner retinal neurons, recovering subcortical, cortical and behavioural visual responses in the absence of trophic effects or retinal inflammation. By conferring sustained light sensitivity to degenerate retinas after a single injection, and with the potential for high spatial resolution, P3HT NPs provide a new avenue in retinal prosthetics with potential applications not only in retinitis pigmentosa, but also in age-related macular degeneration.Semiconducting polymer nanoparticles can act as light-sensitive interfaces with retinal neurons, and on microinjection in the eye, rescue vision in retinas affected by photoreceptor degeneration, offering a potential new treatment option for inherited retinal dystrophies and late-stage age-related macular degeneration.
Journal Article
Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network
Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-todisc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.
Journal Article
Photovoltaic retinal prosthesis with high pixel density
Retinal degenerative diseases lead to blindness due to loss of the ‘image capturing’ photoreceptors, while neurons in the ‘image-processing’ inner retinal layers are relatively well preserved. Electronic retinal prostheses seek to restore sight by electrically stimulating the surviving neurons. Most implants are powered through inductive coils, requiring complex surgical methods to implant the coil-decoder-cable-array systems that deliver energy to stimulating electrodes via intraocular cables. We present a photovoltaic subretinal prosthesis, in which silicon photodiodes in each pixel receive power and data directly through pulsed near-infrared illumination and electrically stimulate neurons. Stimulation is produced in normal and degenerate rat retinas, with pulse durations of 0.5–4 ms, and threshold peak irradiances of 0.2–10 mW mm
−2
, two orders of magnitude below the ocular safety limit. Neural responses were elicited by illuminating a single 70 µm bipolar pixel, demonstrating the possibility of a fully integrated photovoltaic retinal prosthesis with high pixel density.
Researchers demonstrate, in both normal and degenerate rat retinas, a photovoltaic subretinal prosthesis in which the silicon photodiodes in each pixel receive power and data through pulsed near-infrared illumination.
Journal Article
Comparison between direct contact and extract exposure methods for PFO cytotoxicity evaluation
A series of recent acute blindness cases following non–complicated retinal detachment surgery caused the release of several health alerts in Spain. The blindness was attributed to certain lots of perfluoro-octane (PFO; a volatile and transient medical device). Similar cases have been reported in other countries. This has raised questions regarding the validity of cytotoxicity test methods currently used to certify the safety of PFO lots. The tests were performed according to the International Organization for Standardization (ISO) norms, using the extract dilution method or the indirect contact method as applied to L929 cells, a line derived from mouse fibroblasts. The limitations of those methods have been resolved in this study by proposing a new cytotoxicity test method for volatile substances. The new method requires direct contact of the tested substance with cells that are similar to those exposed to the substance in the clinical setting. This approach includes a few new technical steps that are crucial for detecting cytotoxicity. Our new method detected toxic PFO lots that corresponded to the lots producing clinical blindness, which previous methods failed to detect. The study suggests applying this new method to avoid occurrence of such cases of blindness.
Journal Article