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      Transformative Approaches to Patient Literacy and Healthcare Innovation : 

      Revolutionizing Diabetic Retinopathy Diagnostics and Therapy Through Artificial Intelligence

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      , ,
      IGI Global

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          Abstract

          Using artificial intelligence (AI) to its transformative advantage, the smart vision initiative represents a paradigm shift in the diagnostics and treatment of diabetic retinopathy. The primary aim of this initiative is to address all forms of diabetic retinopathy using cutting-edge AI techniques, including deep neural networks and machine learning. These advanced algorithms are designed for rapid and precise diagnosis, enabling swift interventions to prevent visual impairment by identifying intricate patterns that are invisible to the human eye. Through the identification of complex patterns that are invisible to the human eye, these algorithms guarantee quick and accurate diagnosis. This early detection is crucial as it allows for immediate care, significantly reducing the risk of irreversible vision loss. The smart vision initiative sets the stage for a future where diabetic retinopathy no longer leads to blindness, offering a brighter, clearer, and safer optical future for those affected by the condition.

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          Most cited references55

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          Artificial intelligence and deep learning in ophthalmology

          Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.
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            Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review.

            Diabetes retinopathy (DR) is a leading cause of vision loss in middle-aged and elderly people globally. Early detection and prompt treatment allow prevention of diabetes-related visual impairment. Patients with diabetes require regular follow-up with primary care physicians to optimize their glycaemic, blood pressure and lipid control to prevent development and progression of DR and other diabetes-related complications. Other risk factors of DR include higher body mass index, puberty and pregnancy, and cataract surgery. There are weaker associations with some genetic and inflammatory markers. With the rising incidence and prevalence of diabetes and DR, public health systems in both developing and developed countries will be faced with increasing costs of implementation and maintenance of a DR screening program for people with diabetes. To reduce the impact of DR-related visual loss, it is important that all stakeholders continue to look for innovative ways of managing and preventing diabetes, and optimize cost-effective screening programs within the community.
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              Deep learning in ophthalmology: The technical and clinical considerations

              The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.
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                Author and book information

                Book Chapter
                February 9 2024
                : 136-155
                10.4018/979-8-3693-3661-8.ch007
                7fd7e13d-dd36-49f5-b196-025a1344e05a
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