A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.

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.

Ranibizumab for myopic choroidal neovascularization.

INTRODUCTION: Myopic choroidal neovascularization (CNV) is one of the most vision-threatening complications in patients with pathologic myopia. Over the last decade, anti-angiogenesis therapy with anti-vascular endothelial growth factor (anti-VEGF) agents has become the standard-of-care treatment for myopic CNV and ranibizumab has been approved for treating myopic CNV. AREAS COVERED: Review of preclinical studies and clinical trials data supporting the use of ranibizumab for myopic CNV. Discussion on the mechanisms, efficacy, safety, regulatory affairs, and future directions of ranibizumab for myopic CNV are highlighted. EXPERT OPINION: Ranibizumab has demonstrated good efficacy and safety profile in multiple clinical trials and long-term studies for treating myopic CNV. Cost-effective analysis has shown that ranibizumab therapy is a cost-effective treatment for myopic CNV. Among the currently available anti-VEGF agents, ranibizumab is the only drug that is approved for the treatment of myopic CNV by the US Food and Drug Administration. In the coming few years, biosimilars of ranibizumab may become available and will have the potential to lower the cost of ranibizumab. Long-term visual gain after ranibizumab treatment for myopic CNV is limited by chorioretinal atrophy associated with pathologic myopia and further research is required to tackle the development of chorioretinal atrophy.