Title: Etiology of macular edema defined by deep learning in optical coherence tomography scans

Abstract

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.

Biography

Medical Doctor and Ophthalmologist graduated from the Universidad Nacional de Colombia, Occasional Professor of the Department of Ophthalmology (Faculty of Medicine) of Universidad Nacional de Colombia. I have a great interest in research, with an emphasis on the fields of molecular biology, genetics, and immunology, as well as the development of diagnostic tools based on artificial intelligence. With a passion for learning about these areas and sharing knowledge, I have served as a speaker for residents and ophthalmologists. I have leadership skills, teamwork, responsibility, and strong commitment to the comprehensive well-being of patients.With full willingness to persevere and improve continuously, I have the firm conviction of being more, to serve better.