Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.

ABSTRACT: 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 a 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

SUBMITTER: Holste G 

PROVIDER: S-EPMC11451643 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature