Deep learning, a recently described AI machine learning technique, when applied to image analysis, allows the algorithm to analyze data using multiple processing layers to extract different image features,1x1LeCun, Y., Bengio, Y., and Hinton, G. Deep learning. In ophthalmology, many groups have reported exceptional diagnostic performance using deep learning algorithms to detect various ocular conditions based on anterior segment topography (e.g., keratoconus),5x5Hwang, E.S., Perez-Straziota, C.E., Kim, S.W. et al. Distinguishing highly asymmetric keratoconus eyes using combined Scheimpflug and spectral-domain OCT analysis. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning.
Varadarajan, Avinash, Bavishi, Pinal, Raumviboonsuk, Paisan, Chotcomwongse, Peranut, Venugopalan, Subhashini, Narayanaswamy, Arunachalam, Cuadros, Jorge, Kanai, Kuniyoshi, Bresnick, George, Tadarati, Mongkol, Silpa-archa, Sukhum, Limwattanayingyong, Jirawut, Nganthavee, Variya, Ledsam, Joe, Keane, Pearse A, Corrado, Greg S, Peng, Lily, Webster, Dale R
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema. However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates as a proxy for DME on color fundus photographs, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict DME grades derived from OCT exams. Our "OCT-DME" model had an AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the OCT-DME model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retina specialists. In addition, we used saliency and other techniques to examine how the model is making its prediction. The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging.
This survey paper presents a detailed overview of the applications for deep learning in ophthalmic diagnosis using retinal imaging techniques. The need of automated computer-aided deep learning models for medical diagnosis is discussed. Then a detailed review of the available retinal image datasets is provided. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of red lesions are reviewed.Recent deep learning models for classification of retinal disease including age-related macular degeneration, glaucoma, diabetic macular edema and diabetic retinopathy are also reported.
OCT has profoundly disrupted conventional diagnostic and therapeutic strategies in clinical management and has led to paradigm shifts in the understanding of macular disease. Although OCT has continuously undergone hardware improvements since its inception,1x1Huang, D., Swanson, E.A., Lin, C.P. et al. The number of patients with macular disease requiring efficient disease management based on OCT in clinical practice continues to increase, similarly to the amount of image data produced by advanced OCT technology such as swept source. Therefore, the feasibility of manual OCT assessment in clinical practice has become largely unrealistic. Likewise, poor reproducibility between OCT assessors, even in a research setting, also has been reported.2x2Toth, Specifically, there is a clear need to advance automated analysis beyond a purely anatomic presence/absence detection to an accurate measurement of markers for disease activity.
AI is poised to revolutionize medicine. An overview of the field, with selected applications in ophthalmology. From the back of the eye to the front, artificial intelligence (AI) is expected to give ophthalmologists new automated tools for diagnosing and treating ocular diseases. This transformation is being driven in part by a recent surge in attention to AI's medical potential from big players in the digital world like Google and IBM. But, in ophthalmic AI circles, com puterized analytics are being viewed as the path toward more efficient and more objective ways to interpret the flood of images that modern eye care practices produce, according to ophthalmologists involved in these efforts. The most immediately promising computer algorithms are in the field of retinal diseases.