Learning-based 3D modeling of AMD to assess disease progression and response to treatment

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Abstract: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in developed countries. With the growing aging population, the burden of AMD will continue to rise. Despite significant research efforts, we still have limited understanding of what drives the disease progression and why some patients advance to final stages, not respond to available treatments, and have profound vision loss. To improve visual outcomes for these patients, it is critical to develop methods that can identify individuals with early, asymptomatic changes who are at the risk of developing advanced forms of disease. Consequently, by employing recent developments in deep learning and automated image and video analysis, the goal of this project is to develop computational methods and models for automated image analysis and biomarker identification to improve our understanding of AMD and help us to predict its course. To achieve this, we will develop Deep Neural Network (DNN)-based models to identify useful image biomarkers for AMD from different imaging modalities commonly used in clinical practice – Optical-Coherence Tomography (OCT), Fundus Autofluorescence (FAF), and Fundus Angiography (FA). We hypothesize that adding FAF and FA images will significantly improve localization and classification of pathology; thus, facilitate a better understanding of the disease's natural history and response to treatment. To execute this innovative, high-risk/high-reward project, we will use the data from The Duke Ophthalmic Registry, the largest single-institution clinical database for ophthalmic records in the world. We have access to a downloaded and expert-annotated large image dataset numbering over 6400 patients that meet our study's inclusion criteria – progression of AMD from early to advanced stages (dry and wet). For each patient, OCT, FAF, and FA images were captured during a single visit or within specified follow-up intervals (up to ten years or more). We will develop learning-based methods and models for one-time-point image fusion and analysis; specifically, DNN models for AMD classification (early vs. intermediate vs. advanced dry AMD vs. wet AMD), segmentation of the diseased tissue, and 3D reconstruction of the affected area. Finally, using the longitudinal datasets, we will develop deep-learning methods and models to analyze disease evolution over time. The critical insight and novelty are to consider longitudinal datasets as sequences of 2D and 3D pathology models, which allows for the use of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)-based neural networks, which were recently introduced and employed for time-dependent video frame prediction in the context of autonomous vehicles and human action recognition. The proposed research will result in early AMD patient stratification, which will allow for individually tailored follow-up schedules and lead to timely treatments; this is very important since the early treatment of wet AMD results in better visual outcomes.