Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning.

TitlePredicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning.
Publication TypeJournal Article
Year of Publication2022
AuthorsLee J, Wanyan T, Chen Q, Keenan TDL, Glicksberg BS, Chew EY, Lu Z, Wang F, Peng Y
JournalMach Learn Med Imaging
Volume13583
Pagination11-20
Date Published2022 Sep
Abstract

Accurately predicting a patient's risk of progressing to late age-related macular degeneration (AMD) is difficult but crucial for personalized medicine. While existing risk prediction models for progression to late AMD are useful for triaging patients, none utilizes longitudinal color fundus photographs (CFPs) in a patient's history to estimate the risk of late AMD in a given subsequent time interval. In this work, we seek to evaluate how deep neural networks capture the sequential information in longitudinal CFPs and improve the prediction of 2-year and 5-year risk of progression to late AMD. Specifically, we proposed two deep learning models, CNN-LSTM and CNN-Transformer, which use a Long-Short Term Memory (LSTM) and a Transformer, respectively with convolutional neural networks (CNN), to capture the sequential information in longitudinal CFPs. We evaluated our models in comparison to baselines on the Age-Related Eye Disease Study, one of the largest longitudinal AMD cohorts with CFPs. The proposed models outperformed the baseline models that utilized only single-visit CFPs to predict the risk of late AMD (0.879 vs 0.868 in AUC for 2-year prediction, and 0.879 vs 0.862 for 5-year prediction). Further experiments showed that utilizing longitudinal CFPs over a longer time period was helpful for deep learning models to predict the risk of late AMD. We made the source code available at https://github.com/bionlplab/AMD_prognosis_mlmi2022 to catalyze future works that seek to develop deep learning models for late AMD prediction.

DOI10.1007/978-3-031-21014-3_2
Alternate JournalMach Learn Med Imaging
PubMed ID36656604
PubMed Central IDPMC9842432
Grant ListR00 LM013001 / LM / NLM NIH HHS / United States