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Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images by Bridge, Joshua; Harding, Simon; Zheng, Yalin is a Medicine article available to read on EtoBox.

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## Objective To develop a prognostic tool to predict the progression of age-related eye disease progression using longitudinal colour fundus imaging. ## Methods and analysis Previous prognostic models using deep learning with imaging data require annotation during training or only use a single time point. We propose a novel deep learning method to predict the progression of diseases using longitudinal imaging data with uneven time intervals, which requires no prior feature extraction. Given previous images from a patient, our method aims to predict whether the patient will progress onto the next stage of the disease. The proposed method uses InceptionV3 to produce feature vectors for each image. In order to account for uneven intervals, a novel interval scaling is proposed. Finally, a recurrent neural network is used to prognosticate the disease. We demonstrate our method on a longitudinal dataset of colour fundus images from 4903 eyes with age-related macular degeneration (AMD), taken from the Age-Related Eye Disease Study, to predict progression to late AMD. ## Results Our method attains a testing sensitivity of 0.878, a specificity of 0.887 and an area under the receiver operati

Who reads Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images?

Author

Bridge, Joshua; Harding, Simon; Zheng, Yalin

Publisher

BMJ; BMJ Publishing Group; [London]: BMJ (ISSN 2397-3269)

Published

2020

Language

EN

Field

Medicine (Health Sciences)