Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS

Gregory Ghahramani, Matthew Brendel, Mingquan Lin, Qingyu Chen, Tiarnan Keenan, Kun Chen, Emily Chew, Zhiyong Lu, Yifan Peng, Fei Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late- AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) "deep-features" are more informative than clinician derived features. We make codes publicly available at https://github.com/bionlplab/AMD_prognosis_amia2021.

Original languageEnglish (US)
Pages (from-to)506-515
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2021
StatePublished - 2021
Externally publishedYes

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