Abstract
Background: Retinal diseases are emerging as a significant health concern, making early detection and prompt treatment crucial to prevent visual impairment. Optical coherence tomography (OCT) is the preferred imaging modality for non-invasive diagnosis. Both diabetic macular edema (DME) and macular edema secondary to retinal vein occlusion (RVO) present an increase in retinal thickness, posing etiologic diagnostic challenges for non-specialists in retinal diseases. The lack of research on deep learning classification of macular edema secondary to RVO using OCT images motivated us to propose a convolutional neural network model for this task. Methods: The VGG-19 network was fine-tuned with a public dataset to classify OCT images. This network was then used to develop three models: unimodal-the input is only the OCT B-scan; multimodal-the inputs are the OCT B-scan and diabetes information, and multi-image-the inputs are the OCT B-scan, the infrared image, and the diabetes information. Seven hundred sixty-six patients from ULS São João were selected, comprising 208 healthy eyes, 207 with macular edema secondary to RVO, 218 with DME, and 200 with other pathologies. The performance metrics include accuracy, precision, recall, F(0.5) score, and area under the receiver operating characteristic curve (AUROC). Results: The multi-image model achieved better results, with an accuracy of 95.20%, precision of 95.43%, recall of 95.20%, F(0.5)-score of 95.32%, F(1)-score of 95.21%, and AUROC of 99.59% on the classification task between four classes. Conclusions: This study presents a novel method to distinguish macular edema secondary to RVO and DME using diabetes diagnosis, OCT, and infrared images. This research aims to provide a reliable tool for ophthalmologists, improving the accuracy and speed of diagnoses.