![]() The IDX-DR system improved on the IDP by the addition of DL features. There were a total of six false negatives, none of which met treatment criteria. The sensitivity for detecting referable diabetic retinopathy (rDR) against the consensus of three retinal expert graders was 96.8%, and the specificity was 59.4$. Similarly, the IDP has been verified against the Messidor-2 dataset, a publicly available, de-identified set of digital fundus colour images of 1748 eyes in 874 patients with diabetes. Notably, none of the false negatives were assessed as having sight-threatening DED by human graders. Discounting ungradable patients, the IDP sensitivity of 86.7% and the specificity of 70.0% were comparable to that of the human graders. In 20 cases, human graders decided that the image quality was insufficient but IDP analysed the images as gradable with no diabetic eye disease (DED). In 334 cases, the images were deemed to be of insufficient quality by human graders and IDP. In an analysis done on images from the Nakuru Eye Study in Kenya, images from 3640 participants were analysed by the IDP algorithm as well as human graders. IDP has shown good results in Caucasian, North African, and Sub-Saharan populations. Earlier versions of IDx-DR have been studied as part of the Iowa Detection Programme (IDP), and included separate algorithms for quantifying image quality and the detection of haemorrhages, exudates, cotton wool spots, neovascularisation, and irregular lesions. The IDx-DR system combines results from multiple, partially dependent biomarker detectors, some of which utilise convolutional neural networks. This review aims to summarise state-of-art DR screening technologies that have been published in the literature thus far. Recent works on DL in ophthalmology showcase its potential to at least partially replace human graders, while providing a similar level of accuracy. Such artificial intelligence (AI) systems have been demonstrated to lower cost, improve diagnostic accuracy, and increase patient access to DR screening. ![]() For medical imaging analysis in general, it has achieved robust results in various medical specialities such as radiology and dermatology for ophthalmology in particular, deep learning (DL) continues the long tradition of autonomous and assisted analysis of retinal photographs, which has existed since the 1990s and before. ![]() It has been widely adopted in many domains including social media, tele-communications, cybersecurity, and medicine. In addition, in developing countries, the cost of such systems can put substantial strain on the healthcare systems when both financial and human resources are often in short supply.ĭeep learning, a state-of-art machine learning (ML) technique, has shown promising diagnostic performance in image recognition, speech recognition, and natural language processing. However, the diagnostic accuracy achieved may not be optimal, and scaling and sustaining such systems has been found to be challenging. Such programmes are mostly based on the analysis of fundus photographs by specially trained graders, often through telemedicine. There is substantial scientific evidence that early diagnosis and timely treatment can prevent most visual loss from DR, and developed countries have therefore established DR screening programmes aimed at early diagnosis, surveillance and timely treatment of DR. The prevalence of diabetic retinopathy (DR), a primary cause of blindness and vision loss worldwide, was estimated at 93 million in 2012-out of which 28 million people had vision-threatening DR-and this is also expected to further increase. Diabetes is becoming a global epidemic with the number of people affected worldwide rising from 108 million in 1980 to an estimated 425 million in 2017, and an estimated 629 million in 2045.
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