Background/aimsFundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven among different ophthalmologists. The aim of the study is to develop a clinically usable multilevel classification deep learning model for FFA images, including prediagnosis assessment and lesion classification.MethodsA total of 15 599 FFA images of 1558 eyes from 845 patients diagnosed with DR were collected and annotated. Three convolutional neural network (CNN) models were trained to generate the label of image quality, location, laterality of eye, phase and five lesions. Performance of the models was evaluated by accuracy, F-1 score, the area under the curve and human-machine comparison. The images with false positive and false negative results were analysed in detail.ResultsCompared with LeNet-5 and VGG16, ResNet18 got the best result, achieving an accuracy of 80.79%–93.34% for prediagnosis assessment and an accuracy of 63.67%–88.88% for lesion detection. The human-machine comparison showed that the CNN had similar accuracy with junior ophthalmologists. The false positive and false negative analysis indicated a direction of improvement.ConclusionThis is the first study to do automated standardised labelling on FFA images. Our model is able to be applied in clinical practice, and will make great contributions to the development of intelligent diagnosis of FFA images.
Purpose: This study aimed to determine the efficacy of a multimodal deep learning (DL) model using optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images for the assessment of choroidal neovascularization (CNV) in neovascular age-related macular degeneration (AMD). Methods: This retrospective and cross-sectional study was performed at a multicentre, and the inclusion criteria were age >50 years and a diagnosis of typical neovascular AMD. The OCT and OCTA data for an internal data set and two external data sets were collected. A DL model was developed with a novel feature-level fusion (FLF) method utilized to combine the multimodal data. The results were compared with identification performed by an ophthalmologist. The best model was tested on two external data sets to show its potential for clinical use. Results: Our best model achieved an accuracy of 95.5% and an area under the curve (AUC) of 0.9796 on multimodal data inputs for the internal data set, which is comparable to the performance of retinal specialists. The proposed model reached an accuracy of 100.00% and an AUC of 1.0 for the Ningbo data set, and these performance indicators were 90.48% and an AUC of 0.9727 for the Jinhua data set.
Conclusion:The FLF method is feasible and highly accurate, and could enhance the power of the existing computer-aided diagnosis systems. The bi-modal computer-aided diagnosis (CADx) system for the automated identification of CNV activity is an accurate and promising tool in the realm of public health.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.