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Artificial Intelligence helping diabetic patients keep their sight

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Artificial intelligence is helping doctors accurately diagnose eye disease in 96 per cent of cases at Dubai Diabetes Centre.


Artificial Intelligence Can Support Ophthalmologists, Not Replace Them

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A new study published in the April edition of Ophthalmology, the journal of the American Academy of Ophthalmology, found that when it comes to diagnosing diabetic retinopathy, physicians and artificial intelligence (AI) systems working together are more effective than either alone. While earlier research has focused on how AI systems perform compared with human specialists, this study goes further to investigate how AI can be used in a real-world clinical setting. The objective was to see if the combination of a doctor and an algorithm performed better in diagnosis than a doctor alone or the algorithm alone. The April Ophthalmology study evaluated 10 ophthalmologists of different training and experience grading 1,796 eye pictures of diabetic patients, from normal to severe. The graders read images three ways: 1) without the algorithm, 2) with the algorithm grade, or 3) with the algorithm grade plus an explanation of why the algorithm produced that grade.


This AI screening tool for diabetic retinopathy makes a decision, not a recommendation - MedCity News

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Artificial intelligence is a healthcare and technology buzzword right now, but IDx Founder and President Michael Abràmoff is not a Johnny-come-lately to this phenomenon. His journey and that of the company's lead product began over two decades ago in the Netherlands.


Strategies to Tackle the Global Burden of Diabetic Retinopathy: From Epidemiology to Artificial Intelligence

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Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in low- and middle-income countries. Diabetic retinopathy (DR) affects 1 in 3 people with diabetes and remains the leading cause of blindness in working-aged adults. There are 3 broad strategic imperatives to prevent blindness caused by DR. Primary prevention requires preventing or delaying the onset of DR in those with diabetes by systems-level lifestyle modifications such as increasing physical activity or dietary modifications, pharmacological interventions for glycaemic and blood pressure control, and systematic screening for the onset of DR. Secondary prevention requires preventing the progression of DR in patients with DR by continuing systemic risk factor control, regular screening to monitor for the progression of mild DR to vision-threatening stages, and the development and implementation of evidence-based guidelines for managing DR. In this aspect, telemedicine-based DR screening incorporating artificial intelligence technology has the potential to facilitate more widespread and cost-effective screening, particularly in low- and middle-income countries. Tertiary prevention of DR blindness has been the main focus of the clinical ophthalmology community, classically based on laser photocoagulation treatment and ocular surgery but with an increasing use of anti-vascular endothelial growth factor (anti-VEGF) for vision-threatening DR. Evidence from serial epidemiological studies shows blindness due to DR has declined in high-income countries (e.g., the USA and UK) due to coordinated public health education efforts, increased awareness, early detection by DR screening, sustained systemic risk factor control, and the availability of effective tertiary level treatment. However, the progress made in reducing DR blindness in high-income countries may be overwhelmed by the increasing numbers of patients with diabetes and DR in low- and middle-income countries (e.g., China, India, Indonesia, etc.).


Case for Automated Detection of Diabetic Retinopathy

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Diabetic retinopathy, an eye disorder caused by diabetes, is the primary cause of blindness in America and over 99% of cases in India. India and China currently account for over 90 million diabetic patients and are on the verge of an explosion of diabetic populations. This may result in an unprecedented number of persons becoming blind unless diabetic retinopathy can be detected early. Aravind Eye Hospitals is the largest eye care facility in the world, handling over 2 million patients per year. The hospital is on a massive drive throughout southern India to detect diabetic retinopathy at an early stage. To that end, a group of 10-15 physicians are responsible for manually diagnosing over 2 million retinal images per year to detect diabetic retinopathy. While the task is extremely laborious, a large fraction of cases turn out to be normal indicating that much of this time is spent diagnosing completely normal cases. This paper describes our early experiences working with Aravind Eye Hospitals to develop an automated system to detect diabetic retinopathy from retinal images. The automated diabetic retinopathy problem is a hard computer vision problem whose goal is to detect features of retinopathy, such as hemorrhages and exudates, in retinal color fundus images. We describe our initial efforts towards building such a system using a range of computer vision techniques and discuss the potential impact on early detection of diabetic retinopathy.