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Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.

Purpose To predict the anti‐vascular endothelial growth factor (VEGF) therapeutic response of diabetic macular oedema (DME) patients from optical coherence tomography (OCT) at the initiation stage of treatment using a machine learning‐based self‐explainable system. Methods A total of 712 DME patients were included and classified into poor and good responder groups according to central macular thickness decrease after three consecutive injections. Machine learning models were constructed to make predictions based on related features extracted automatically using deep learning algorithms from OCT scans at baseline. Five‐fold cross‐validation was applied to optimize and evaluate the models. The model with the best performance was then compared with two ophthalmologists. Feature importance was further investigated, and a Wilcoxon rank‐sum test was performed to assess the difference of a single feature between two groups. Results Of 712 patients, 294 were poor responders and 418 were good responders. The best performance for the prediction task was achieved by random forest (RF), with sensitivity, specificity and area under the receiver operating characteristic curve of 0.900, 0.851 and 0.923. Ophthalmologist 1 and ophthalmologist 2 reached sensitivity of 0.775 and 0.750, and specificity of 0.716 and 0.821, respectively. The sum of hyperreflective dots was found to be the most relevant feature for prediction. Conclusion An RF classifier was constructed to predict the treatment response of anti‐VEGF from OCT images of DME patients with high accuracy. The algorithm contributes to predicting treatment requirements in advance and provides an optimal individualized therapeutic regimen.

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