Automated User Request Operator and Responsive Application (AURORA): A Novel Approach Implementing a Deep Learning Convolutional Neural Network Framework to Detect Choroidal Neovascularization, Diabetic Macular Edema, and Macular Degeneration

Overview

Over 1.3 billion people worldwide suffer from either vision impairment or blinding retinal diseases, and approximately $139 billion is the estimated economic burden due to these retinal diseases. Machine learning approaches are increasingly helpful in successful image-based diagnosis, disease prognosis, and risk assessment. Retinal optical coherence tomography (OCT) is an imaging technique used to capture high-resolution cross-sections of the retinas of patients. Though it has been demonstrated that an early diagnosis can significantly reduce eyesight difficulties later in life, screenings for retinal diseases are inaccessible to a majority of the world because of their cost ($100-$1000 screening) and the need for specialized scientific equipment. As none of the current models provide a diagnostic accuracy higher than 90%, AURORA was conceptualized. The purpose of this project was to create AURORA, the Automated User Request Operator and Responsive Application, a diagnostic tool based on a deep-learning framework for the screening of patients with common blinding retinal diseases. This framework utilizes transfer learning (in both training and testing programs), which trains a neural network with a fraction of the data of conventional approaches. AURORA was trained on the dataset of greyscale OCT images repository at the open-source dataset platform, Kaggle. AURORA may ultimately aid in accelerating the diagnosis of these retinal diseases, thereby facilitating earlier treatment, resulting in improved clinical outcomes.

Competition history

• ISEF 2020 Robotics and Intelligent Machines · Entry ROBO034

Resources

• ISEF abstract page ↗

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