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

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. Finall

SUBMITTER: Bridge J 

PROVIDER: S-EPMC7566421 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature