Background/aims The aim of this study was to generate and evaluate individualised post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of antivascular endothelial growth factor therapy for typical neovascular age-related macular degeneration (nAMD) based on pretherapeutic images using generative adversarial network (GAN). Methods A total of 476 pairs of pretherapeutic and post-therapeutic OCT images of patients with nAMD were included in training set, while 50 pretherapeutic OCT images were included in the tests set retrospectively, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. The pix2pixHD method was adopted for image synthesis. Three experiments were performed to evaluate the quality, authenticity and predictive power of the synthetic images by retinal specialists. Results We found that 92% of the synthetic OCT images had sufficient quality for further clinical interpretation. Only about 26%–30% synthetic post-therapeutic images could be accurately identified as synthetic images. The accuracy to predict macular status of wet or dry was 0.85 (95% CI 0.74 to 0.95). Conclusion Our results revealed a great potential of GAN to generate post-therapeutic OCT images with both good quality and high accuracy.

Background/aims To evaluate the efficacy of antivascular endothelial growth factor (anti-VEGF) agents pretreatment before vitrectomy for patients with complicated proliferative diabetic retinopathy (PDR). Methods The PubMed, Embase and the Cochrane Central Register of Controlled Trials were searched up to June 2017 to identify related studies. The Peferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were followed. The StataSE V.12.0 software was used to analyse the relevant data. The weighted mean difference, relative risk and their 95% CIs were used to assess the strength of the association. Results 14 randomised controlled trials involving 613 patients were assessed, the anti-VEGF pretreatment group included 289 patients and the control group included 324 patients. Our analysis indicated that anti-VEGF pretreatment before vitrectomy for complicated PDR could facilitate much easier surgery regarding less intraoperative bleeding, less endodiathermy, shorter duration of surgery, less iatrogenic retinal breaks, less frequency of using silicone oil and relaxing retinotomy (P<0.05). Additionally, anti-VEGF pretreatment could also achieve better postoperative best-corrected visual acuity, less early recurrent vitreous haemorrhage (VH) and quicker absorption of recurrent VH (P0.05). Conclusion The pretreatment of anti-VEGF agents before vitrectomy for patients with complicated PDR might facilitate much easier surgery and better visual rehabilitation, reduce the rate of early recurrent VH and accelerate its absorption. Moreover, future better-designed studies with larger sample sizes are required to further evaluate the efficacy of different anti-VEGF agents and reach a firmer conclusion.

Aims To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images. Methods A retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset. Results On a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well. Conclusion A bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.