Purpose: To develop an automated method based on Deep Learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans. Methods: A total of 4,230 images were obtained from data repositories of patients attended in an Ophthalmology Clinic in Colombia and two free open-access databases. They were annotated with four biomarkers (BMs) as intraretinal fluid, subretinal fluid, hyperreflective foci/tissue and drusen. Then, the scans were labeled as control or ocular disease among diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), and retinal vein occlusion (RVO) by two expert ophthalmologists. Our method was developed by following four consecutive phases: segmentation of BMs, the combination of BMs, feature extraction with convolutional neural networks to achieve binary classification for each disease, and finally, multiclass classification of diseases and control images. Results: The accuracy of our model for nAMD was 97%, and for DME, RVO and control were 94%, 93%, and 93% respectively. Area under curve values were 0.99, 0.98, 0.96, and 0.97 respectively. The mean Cohen’s Kappa Coefficient for the multiclass classification task was 0.84. (Table 1). The calculation of the Gradient Weighted Class Activation Map (Grad-CAM) was implemented to provide a visual explanation of the more relevant areas to classify into a disease.Figure 1 shows model interpretability with feature maps as heat maps, where red zones mean activated parts of our method. Conclusion: The proposed DL model may identify OCT scans as normal and ME. In addition, it may classify its cause between three major exudative retinal diseases with high accuracy and reliability. Our DL approach can optimize the efficiency and timeliness of appropriate etiological diagnosis of ME, thus improving patient access and clinical decision making. It could be useful in places with a shortage of specialists and for readers that evaluate OCT scans remotely.
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