Foundation model-driven distributed learning for enhanced retinal age prediction.
Journal:
Journal of the American Medical Informatics Association : JAMIA
PMID:
39225790
Abstract
OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction.