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OBJECTIVE Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. RESEARCH DESIGN AND METHODS We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia. RESULTS In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary data set and >0.894 for the external data sets. CONCLUSIONS We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.

Purpose: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images.Methods: This study included 7,194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision.Results: For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves .0.948 and area under the precision-recall curves .0.866 for the gradability assessment, area under the receiver operating characteristic curves .0.960 and area under the precision-recall curves .0.822 for the measurability assessment, and area under the receiver operating characteristic curves .0.939 and area under the precision-recall curves .0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification.Conclusion: Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss.RETINA 42:184-194, 2022D iabetic macular ischemia (DMI), an important category of diabetic retinopathy (DR), is characterized by enlargement of the foveal avascular zone (FAZ) and retinal capillary loss in other noncontiguous areas of the macula ("capillary drop-out") from fluorescein angiography (FA) assessment. 1 Sim et al 2 esti-mated the prevalence of DMI in a cohort of 488 patients with diabetes mellitus (DM) who were either without DR or with different stages of DR, and they found that the prevalence of DMI was 41.3%. Diabetic macular ischemia has been implicated as a risk factor for DR progression 2 and a prognosis factor for thera-184

Purpose: Retinal vein occlusion (RVO) is the second most common cause of vision loss after diabetic retinopathy due to retinal vascular disease. Retinal nonperfusion (RNP), identified on fluorescein angiograms (FA) and appearing as hypofluorescence regions, is one of the most significant characteristics of RVO. Quantification of RNP is crucial for assessing the severity and progression of RVO. However, in current clinical practice, it is mostly conducted manually, which is time-consuming, subjective, and error-prone. The purpose of this study is to develop fully automated methods for segmentation of RNP using convolutional neural networks (CNNs). Methods: FA images from 161 patients were analyzed, and RNP areas were annotated by three independent physicians. The optimal method to use multi-physicians' labeled data to train the CNNs was evaluated. An adaptive histogram-based data augmentation method was utilized to boost the CNN performance. CNN methods based on context encoder module were developed for automated segmentation of RNP and compared with existing state-of-the-art methods. Results: The proposed methods achieved excellent agreements with physicians for segmentation of RNP in FA images. The CNN performance can be improved significantly by the proposed adaptive histogram-based data augmentation method. Using the averaged labels from physicians to train the CNNs achieved the best consensus with all physicians, with a mean accuracy of 0.883AE0.166 with fivefold cross-validation. Conclusions: We reported CNN methods to segment RNP in RVO in FA images. Our work can help improve clinical workflow, and can be useful for further investigating the association between RNP and retinal disease progression, as well as for evaluating the optimal treatments for the management of RVO.

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