Foundation model-driven distributed learning for enhanced retinal age prediction.

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.

Materials and methods

The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predic

SUBMITTER: Nielsen C 

PROVIDER: S-EPMC11491655 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature