Background Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs). Objective This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology. Methods To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020. Results We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease. Conclusions Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.
Introduction: Pharmacist's knowledge about different aspects of this pandemic is crucial because it influence their role and contribution as a frontline health care provider, as pharmacies and most of the pharmacy practice sectors are kept open even during lockdowns providing counseling, patient care. Pharmacist can provide valuable services during COVID-19 pandemic, these services may include: provide reliable information on the disease, participate in public education on preventive measures, referring of suspected cases, insuring continuous supply of medicine. Methods: A web-based, cross-sectional study, conducted using survey instrument to obtain responses from Sudanese pharmacists during the period from 26th of May to 3rd of June 2020. A 14-item survey instrument was developed. The web-based cross-sectional study was carried out among Sudanese pharmacists. A self-reported structured questionnaire was divided into three sections: demographic characteristics, questions assessing the knowledge, and one question for the pharmacist contribution during the pandemic. Results: The study showed that 51.1% of pharmacists have good knowledge about the COVID-19. The work experience and education level significantly (P<0.05) influence pharmacist knowledge. Majority of pharmacists contribute to different activities during the pandemic, e.g. providing patients with transmission information (94%), Provide factual and reliable information on the diseases symptoms (93.1%), providing patients with prevention information (91.1%). Conclusion: The study identified that pharmacists have good knowledge about COVID-19 pandemic Also pharmacists contributed in many activities as a frontline health care provider during this pandemic.
BACKGROUND Glaucoma means irreversible blindness. Globally, it is the second retinal disease leading to blindness, just preceded by the cataract. Therefore, there is a great need to avoid the silent growth of such disease using the recently developed Generative Adversarial Networks(GANs). OBJECTIVE This paper aims to introduce GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome in order to implement this kind of technology. METHODS To organize this review comprehensively, we used the keywords: ("Glaucoma", "optic disc", "blood vessels") and ("receptive field", "loss function", "GAN", "Generative Adversarial Network", "Deep learning", "CNN", "convolutional neural network" OR encoder), in different variations to gather all the relevant articles from five highly reputed databases: IEEE Xplore, Web of Science, Scopus, Science Direct, and Pubmed. These libraries broadly cover technical and medical literature. For the latest five years of publications, we only included those within that period. Researchers who used OCT or visual fields in their work were excluded. However, papers that used 2D images were included. A large-scale systematic analysis was performed, then a summary was generated. The study was conducted between March 2020 and November 2020. RESULTS We found 59 articles after a comprehensive survey of the literature. Among 59 articles, 29 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. Twenty-nine journal articles discuss recent advances in generative adversarial networks, practical experiments, and analytical studies of retinal disease. CONCLUSIONS Recent deep learning technique, namely generative adversarial network, has shown encouraging retinal disease detection performance. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. There is no existing systematic review paper on retinal disease utilizing generative adversarial networks to the extent of our knowledge. Two paper sets were reported; the first involves surveys on the recent development of GANs or overviews of papers reported in the literature applying machine learning techniques on retinal diseases. While in the second group, researchers have sought to establish and enhance the detection process through generating as real as possible synthetic images with the assistance of GANs. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants to improve further and strengthen future work. Finally, the new directions of this research have been identified.
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