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A research on Artificial Intelligence (AI) applications in healthcare has gained attention over a significant period of time. According to Quer et al. (2017), AI is regarded as the ability of the computer controlled robot or a digital computer in performing tasks linked to intelligent beings. AI is commonly applicable in project development systems said to be endowed with potential intellectual processes. On the other hand, Nelson (2018) describes ophthalmology as a significant branch of surgery and medicine that focuses on diagnosis a well as treatment of the evident eye disorders. A partial list associated to ophthalmology includes macular degeneration, cataract, dry eyes, proptosis, glaucoma and diabetic retinopathy.
The study of AI and ophthalmology is more pertinent and relevant when handling such areas like healthcare, pattern recognition and data management among many others. According to Akkara and Kuriakose (2019), both machine learning as well as artificial intelligence are said to have made their entry in healthcare and modern life. At the same time, ophthalmology is a growing field that is engaging measurable data and imaging, which makes ideal for AI and machine learning application. Apparently, the medical field, pediatrics, ophthalmology and endocrinology are said to have joined the unstoppable AI revolution. For ophthalmology, AI has been more relevant in the analytical process of the retinal fundus images associated to diabetic retinopathy. This can be followed by what is referred as age-related macular degeneration, retinopathy of prematurity and glaucoma. Based on the findings made by Nelson (2018), major technological companies have taken strides towards AI and ophthalmic use. The IBM’s AI, for instance, has the capacity of predicting the visual field data associated to OCT scans. In addition, the DeepMind Health indicated that it is possible with AI to detect the fifty eye disease among others.
Lu et al. (2018) asserted that healthcare has emerged as a significant area at the centre of the AI application. A range of studies have been linked to DL algorithms said to be performed at high levels. This has been applicable to breast histopathology analysis as well as skin cancer classification. Other areas of concern as noted by Lu et al. (2018) include ophthalmology, lung cancer detection and cardiovascular risk prediction. With a vast range of applications, it is paramount to note that AI in ophthalmology is imminent and unstoppable following the development of the AI algorithms and accessible data sets like Messidor, EyePACS and Kaggle’s data set. Subsequent observations made by Du et al. (2018) indicate that AI can be applied in terms of DL and Conventional Machine Learning (CML). This has bolstered subsequent diagnosis of the ocular diseases which covers the most leading causes of blindness diabetic retinopathy, cataract, age-related macular degeneration as well as glaucoma. CML approaches, said to have been introduced by Sandrina Nunes and Miguel Caixinha, have been paramount in monitoring as well as diagnosis of the multimodal ocular diseases. The CML largely attracts small data sets but it can turn out to be cumbersome when it comes to handling visual features. DL, as part of the AI application, is known for having the ability of discovering the most intricate structures across data sets even without specifying the rules. The network for DL is NN thereby it exhibits multiple layers introduced between the output and the input.
Further observations made by Lu et al. (2018) noted that current studies are putting more focus on machine learning, which can attain satisfactory outcomes. More focus on AI and diabetic retinopathy has attracted towards retinal microvasculature, which leads to damage. More people are essentially affected by DR and this has been turned into a public health problem across the world. Large scale screening of the diabetic retinopathy is on high demand as far as treatment and management is put into consideration. Practitioners have consistently called for early intervention, which taps into DR automatic identification. Further attention given to neovascularization detection, microaneurysm, cotton wool spot, hemorrhage and exudation has raised hopes of AI application. In this case, computers can receive images which can be labeled as diagnostic lesions before identifying the final judgment and input images.
More attention given to AI and retinal vein occlusion by Du et al. (2018) indicated that RVO has a prevalence said to range from around 0.3 -2.1% across different people. Direct causes of RVO are believed to involve the sclerotic retinal artery, which compresses the retinal vein. Therefore, diagnosis of RVO is essentially regarded as crucial as far as vision recovery is put into consideration. The intervention of AI taps into automatic diagnosis, which is a process that can benefit benefits suffering from ophthalmology. Besides, Ting et al. (2019) noted that adoption of DL in natural language processing, image recognition and speech recognition has impacted the approach towards healthcare. Apparently, in ophthalmology, the application of DL in visual fields, fundus photographs as well as optical coherence tomography has led to achievement of robust classification performance.
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