Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration

Abstract

Abstract. Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. The presence of drusen is the hallmark of early/intermediate AMD, and their sudden regression is strongly associated with the onset of late AMD. In this work we propose a predictive model of drusen regression using optical coherence tomography (OCT) based features. First, a series of automated image analysis steps are applied to segment and characterize individual drusen and their development. Second, from a set of quantitative features, a random forest classier is employed to predict the occurrence of individual drusen regression within the following 12 months. The predictive model is trained and evaluated on a longitudinal OCT dataset of 44 eyes from 26 patients using leave-one-patient-out cross-validation. The model achieved an area under the ROC curve of 0.81, with a sensitivity of 0.74 and a specicity of 0.73. The presence of hyperreective foci and mean drusen signal intensity were found to be the two most important features for the prediction. This preliminary study shows that predicting drusen regression is feasible and is a promising step toward identication of imaging biomarkers of incoming regression.

Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging

“…It presents a substantial extension of our previous work on this topic. 9 For the learning, we utilized a longitudinal dataset from a prospective observational study, consisting of OCT images of 61 eyes with early/ intermediate AMD, acquired at 3-month intervals. We developed an OCT-based drusen characterization using automated image analysis methods of the outer retina, with a focus on its shape, local appearance of its structure, and that of the overlying neurosensory layers, as well as its short-term longitudinal change.…”