Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease.

ABSTRACT:

Purpose

Deep learning (DL) is a technique explored within ophthalmology that requires large datasets to distinguish feature representations with high diagnostic performance. There is a need for developing DL approaches to predict therapeutic response, but completed clinical trial datasets are limited in size. Predicting treatment response is more complex than disease diagnosis, where hallmarks of treatment response are subtle. This study seeks to understand the utility of DL for clinical problems in ophthalmology such as predicting treatment response and where large sample sizes for model training are not available.

Materials and methods

Four DL architectures were trained using cross-validated transfer learning to classify ultra-widefield angiograms (UWFA) and fluid-compartm

SUBMITTER: Dong V 

PROVIDER: S-EPMC9894083 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature