Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images. Issue 1 (15th October 2020)

• Record Type: Journal Article
• Authors: Bridge, Joshua; Harding, Simon; Zheng, Yalin
• Is Part Of: BMJ open ophthalmology. Volume 5:Issue 1(2020)
• Publication Date: 2020-10-15
• Subjects: retina -- imaging; Ophthalmology -- Periodicals; 617.7005
• DOI: 10.1136/bmjophth-2020-000569
• Languages: English
• ISSNs: 2397-3269

Abstract

Abstract : 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 operating characteristic of 0.950. We compare our method to previous methods, displaying superior performance in our model. Class activation maps display how the network reaches the final decision. Conclusion: The proposed method can be used to predict progression to advanced AMD at some future visit. Using multiple images at different time points improves predictive performance.