  Deep learning has enabled breakthroughs in automated diagnosis from medical
imaging, with many successful applications in ophthalmology. However, standard
medical image classification approaches only assess disease presence at the
time of acquisition, neglecting the common clinical setting of longitudinal
imaging. For slow, progressive eye diseases like age-related macular
degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo
repeated imaging over time to track disease progression and forecasting the
future risk of developing disease is critical to properly plan treatment. Our
proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic
disease prognosis from longitudinal medical imaging, modeling the time to
disease from sequences of fundus photography images captured over long,
irregular time periods. Using longitudinal imaging data from the Age-Related
Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA
significantly outperformed a single-image baseline in 19/20 head-to-head
comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A
temporal attention analysis also suggested that, while the most recent image is
typically the most influential, prior imaging still provides additional
prognostic value.
