Discovery and Validation of AMD Biomarkers for Progression Using Deep Learning

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Project Abstract Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Currently there are no proven effective therapies for treatment of advanced non-neovascular AMD, termed geographic atrophy (GA). Earlier intervention may be preferable, but this would require identification of those individuals with the highest risk for progression to atrophy. Over the last two decades, various studies including ours have identified several optical coherence tomography (OCT)-based factors that appear to associate with a higher risk for AMD progression. The central hypothesis of this proposal is that a deep learning - artificial intelligence (AI) construct can objectively and automatically learn and quantify the most important risk factors, yielding a better prediction of AMD progression risk than current subjectively specified features. In this proposal, we will first develop an AI- based system to automatically identify the “subjectively-specified” high risk factors based on individual spectral domain (SD) OCT 2D scans, and to automatically segment GA (the end-stage outcome variable of AMD) in OCT 2D en face maps. Subsequently, as a proof-of-concept study of our hypothesis, we will apply an AI-based “reverse learning” approach to objectively learn and identify AMD high risk factors in longitudinal OCT data. To achieve these objectives, we will pursue the following specific aims: Aim 1: Develop and validate an AI approach to classify individual OCT 2D scans as containing or not containing the pre-specified risk factor(s). In our previous work, we manually identified the presence or absence of the pre-specific high-risk factors and assigned to a risk score based on the entire OCT volume. Such approach was time consuming and not precise. In this proposal, an AI algorithm will be applied to detect the high risk factors from individual OCT scans. Hence, the precision of the scoring system can be greatly enhanced with high computational complexity. Aim 2: Develop and validate an AI approach to segment GA lesions from 2D OCT en face maps. For the OCT volumes having atrophy, we will perform the GA segmentation from the choroidal hypertransmission-resulted en face map using the multi-scale CNNs. Aim 3: Develop and validate an AI “reverse learning” approach to objectively identify the high risk factors using longitudinal OCT data. The “reverse learning” will be based on the multiple CNNs, followed by de- convolutional networks to identify the high risk factors objectively. Our previous scoring system will possibly be refined and optimized by the potential inclusion (or substitution) of novel risk factors derived from our objective AI approaches. The work in this proposal will be performed retrospectively in SD-OCT images from the image data pool that the multi-PI Sadda has aggregated over years as the director of the Doheny image reading center.