Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications.

Diabetic retinopathy (DR) is a major public health problem and the leading cause of vision loss in the working age population. This paper presents a novel deep learning system for the detection and diagnosis of DR using optical coherence tomography (OCT) images. The input for this system is three-channel local and global information from OCT images. The local high-level information is represented by the thickness channel and the reflectivity channel. The global low-level information is represented by the grey-level OCT original image. The deep learning system processes the three-channel input to produce the final DR diagnoses. Experimental results on 200 OCT images, augmented to 800 images, which are collected by the University of Louisville, show high system performance related to other competing methods. Moreover, 10-fold and leave-one-subject-out (LOSO) experiments are performed to confirm how significant using the fused images is in improving the performance of the diagnoses, by investigating four different CNN architectures. All of the four architectures achieve acceptable performance and confirm a significant performance improvement using the fused images. Using LOSO, the best network performance has improved from 90.1 ± 2% using only the grey level dataset to 97.7 ± 0.5% using the proposed fused dataset. These results confirm the promise of using the proposed system for the detection of DR using OCT images.

Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically segmented using a U-net convolutional neural network (U-CNN). For the input of U-CNN, we utilized three novel image descriptors to account for the visual appearance similarity of the vitreous region and other tissues. Namely, we developed an adaptive appearance-based approach that utilizes a prior shape information, which consisted of a labeled dataset of the manually segmented images. This image descriptor is adaptively updated during segmentation and is integrated with the original greyscale image and a distance map image descriptor to construct an input fused image for the U-net segmentation stage. In the second stage, a fully connected neural network (FCNN) is proposed as a classifier to assess the vitreous inflammation severity. To achieve this task, a novel discriminatory feature of the segmented vitreous region is extracted. Namely, the signal intensities of the vitreous are represented by a cumulative distribution function (CDF). The constructed CDFs are then used to train and test the FCNN classifier for grading (grade from 0 to 3). The performance of the proposed pipeline is evaluated on a dataset of 200 OCT images. Our segmentation approach documented a higher performance than related methods, as evidenced by the Dice coefficient of 0.988 ± 0.01 and Hausdorff distance of 0.0003 mm ± 0.001 mm. On the other hand, the FCNN classification is evidenced by its average accuracy of 86%, which supports the benefits of the proposed pipeline as an aid for early and objective diagnosis of uvea inflammation.