8/6/2023 0 Comments Retina scan softwareGoogle also validated a DR detection algorithm which assigns an image a number between 0-1 that correlates with the likelihood of DR in the image. This algorithm utilizes a previously reported reference consensus standard for referable DR, the International Clinical Classification of Diabetic Retinopathy, and provides four outputs: negative (none or mild DR), referable DR, vision threatening DR, and/or low exam quality which noted a limitation in analysis or image quality. In 2016, Abramoff et al reported a comparison of a deep-learning enhanced algorithm for the automated detection of DR to an identical algorithm that did not utilize deep learning and noted improved specificity for referable DR. A notable characteristic of RetmarkerDR is that it can track disease progression by comparing current images to those that were initially screened out, potentially providing insight on progression of DR. RetmarkerDR is another system that has been used for DR screening, by screening out images without DR and sorting images that have DR into a group that requires further human grader assessment. Prior to the deep learning era, AI models have been trained based on “feature-based” learning to detect microaneurysms, hemorrhages, hard or soft exudates and vessel maps. In particular, AI algorithms have been shown to be effective in detecting clinically significant macular edema as well as advanced stages of DR. Validated AI systems for the assessment of DR have been reported to perform diagnostic functions with high sensitivity and specificity. Table 1: A selection of AI algorithms applied in DR It has been implemented in the screening and diagnosis of retinal diseases such as Diabetic Retinopathy (DR), Retinopathy of Prematurity (RoP) and Age-related Macular Degeneration (AMD). Pattern recognition in retinal imaging modalities is well-suited for the application of artificial intelligence to image analysis. Alam et al analyzed quantitative vascular features on OCTA images using a supervised machine learning algorithm and demonstrated that it was able to differentiate stages of diabetic retinopathy, sickle cell retinopathy, and disease versus. OCT Angiography (OCTA) utilizes sequentially acquired OCT-B scans to construct a depth-resolved image of retinal vasculature. The application of deep learning to automated segmentation has enabled accurate detection of features such as intraretinal fluid, subretinal fluid, drusen, pigment epithelial detachment, and geographic atrophy with comparable accuracy as human graders. By setting a threshold above a certain pixel intensity, certain pixels can be accepted as belonging to a vessel. The use of a threshold can be used to segment retinal vessels, which are typically darker than their surroundings. With the advent of OCT in the early 2000s, automated segmentation has been applied to delineate anatomic and pathologic features in retinal diseases. The most common application of AI methods in retina is the detection of disease-related features on color fundus photographs. Each patient comes with a wealth of diagnostic information that can be applied to deep learning analysis. Retina specialists can utilize numerous imaging techniques for the diagnosis and treatment of retinal diseases, including optical coherence tomography (OCT), OCT Angiography, fundus photography, fundus autofluorescence, and others. Imaging Modalities and Image Processing Techniques in Retina Machine learning algorithms, and deep learning systems in particular, have been developed to improve diagnosis and management of various disease entities in ophthalmology. disease) features on its own that may not be visible to the human eye or known by experts. This allows the deep learning system to detect (e.g. Deep learning can be further classified into supervised, semi-supervised, and unsupervised learning. ĭeep learning is a subset of machine learning, where the simple artificial neural network is expanded to include many hidden layers between the input and the output layers, and this is called the Convolutional Neural Network(CNN), which allows for more intricate evaluation of the input. an image with features previously identified through traditional methods), and the machine processes this information and produces the output (e.g. In essence, the machine is presented with an input (e.g. The use of artificial neural networks is one common example. A range of analytical techniques may be incorporated in machine learning. In machine learning, the machine creates its own algorithms by “learning” the associations between the input and the output. Machine learning, a subset of AI, is a method commonly utilized in the development of automated diagnostic systems.
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