A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers

TL;DR

This paper introduces a hybrid deep learning model that accurately predicts the progression of age-related macular degeneration over various timeframes using OCT imaging biomarkers, potentially enabling earlier intervention.

Contribution

It presents a novel RNN-based approach combining radiomics and deep learning to improve AMD progression prediction from OCT scans, outperforming traditional models.

Findings

Achieved 0.96 AUCROC in predicting AMD progression

Outperformed traditional random forest models

Demonstrated potential for early clinical intervention

Abstract

We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-months) up to long-term progression (21-months). Proposed model combines radiomics and deep learning to handle challenges related to imperfect ratio of OCT scan dimension and training cohort size. We considered a retrospective clinical trial dataset that includes 671 fellow eyes with 13,954 dry AMD observations for training and validating the machine learning models on a 10-fold cross validation setting. The proposed RNN model achieved high accuracy (0.96 AUCROC) for the prediction of both short term and long-term AMD progression, and outperformed the traditional random forest model trained. High accuracy achieved by the RNN establishes the ability to identify AMD patients at risk of progressing to advanced AMD at an early stage which could have a high clinical impact as it allows for optimal clinical follow-up, with more frequent screening and potential earlier treatment for those patients at high risk.