A CNN algorithm teaches itself by analysing a labeled training set of expert-graded images and provides a diagnostic output. In deep neuronal learning, the convolutional neural networks (CNNs) learn to perform their tasks through repetition and self-correction. It processes large amount of data and extracts meaningful patterns from them. It is based on learning features from the data. This review aims to compare the current evidences on various DL models for diagnosis of DR.ĭL algorithm is considered as a fourth industrial revolution. Over the past 2 years, there are many evidences on the use of DL algorithms to identify diabetic retinopathy (DR) either a binary model or multi-classifier models. Recently, DL has also been used to identify risk factors associated with cardiovascular diseases (e.g., blood pressure, smoking and body mass index) from retinal photographs. Likewise, the images from a fundus camera, microscope or radiography are being classified by DL and compared with the trained physician. Researchers are using DL to train algorithms to recognize cancerous tissue comparable to trained physicians. AI-chatbots with speech recognition capability have been explored to identify patterns in patient symptoms to form a potential diagnosis. This technique can also be potentially used to detect diseases, as it can identify and classify data, image or a sound. DL mimics an infant’s brain, which is like a sponge and learns through training. Therefore, there is an unmet and urgent need to develop bespoke clinical and cost-effective screening and treatment pathways that can cover at least the majority of the population with diabetes.ĭeep learning (DL) is a new Artificial Intelligence (AI) machine learning technique, and its use in the medical field has generated much interest over the last few years. From the Asia-Pacific perspective, it is currently neither practical nor economical to have trained health care providers screen all 231 million (153 million and 78 million from Western Pacific and Southeast Asia, respectively). Standard retinal cameras are too costly, electronic patient records are non-existent to develop a diabetes register and most importantly, there is significant shortage of trained personnel to capture the retinal images and grade them and to treat them. The primary care infrastructures of most low and medium income countries are in their infancy. In contrast, there are over 70 million people with diabetes in India alone and an equal numbers of pre-diabetic or undiagnosed diabetes. As an example, only about 4 million people with diabetes need to be screened annually for sight threatening complications in the United Kingdom. First and foremost, the absolute numbers of people with diabetes and undiagnosed diabetes are significantly higher than in the United Kingdom. There are several challenges faced by these countries. The re-call and referral pathways are also well-defined with minimal standards set for each severity grade of diabetic retinopathy.Īttempts to replicate these systematic diabetic retinopathy screening programs in low and middle income countries have not been successful. The images are graded systematically by trained trainers and images may be arbitrated if required. In England and Wales, patients with diabetes diagnosed by their General Practitioners are registered in a diabetes register and diabetic retinopathy screening services invite each patient for annual screening of the retina under mydriasis using standard cameras. Screening and timely treatment of these complications have been shown to reduce blindness due to these complications in the countries where these services are well established such as the United Kingdom. Proliferative diabetic retinopathy and diabetic macular edema are the two retinal sight threatening complications of diabetes.
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