Aims: To report on the use of trypan blue (TB) 0.06% for staining the internal limiting membrane (ILM) and epiretinal membrane (ERM) during vitrectomy and report on their histology. Method: 14 consecutive patients with idiopathic macular hole or macular pucker (seven patients each) were prospectively recruited for ILM or ERM peel respectively. After pars plana vitrectomy and induction of posterior vitreous detachment, 0.5 ml TB 0.06% in phosphate buffered saline (VisonBlue) was injected over the posterior pole in an air filled eye and left for 2 minutes. The stained tissue was peeled with intraocular forceps. Specimens were evaluated using histochemical and immunohistochemical methods. Results: The average follow up was 4.4 months. Internal limiting membranes and epiretinal membranes were stained satisfactorily in all cases and removed successfully. Eight patients (57%) had improvement of 2 or more Snellen lines. All seven macular holes closed. In the ERM cases, no residual membranes were observed clinically, at the latest follow up. No complications relating to the use of the dye were encountered intraoperatively or postoperatively. Of the 14 procedures, nine (four macular hole and five macular pucker) yielded sufficient tissue for histopathological evaluation. Histological and immunohistological assessment revealed that the morphology of these specimens was similar to that observed in macular hole ILM and macular pucker ERM removed without the aid of dye. Conclusion: TB staining facilitated the identification and delineation of ILM and ERM removal during the surgical management of macular holes and macular pucker. The visual outcome of this series and the specimens removed suggest they are no different from those without TB staining. Its use in posterior segment appears to be safe but further studies are required to investigate its long term safety.

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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.

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