Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images.

ABSTRACT:

Importance

Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression.

Objective

To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images.

Design, setting and participants

Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022.

Exposure

Automated ML models were generated from ba

SUBMITTER: Silva PS 

PROVIDER: S-EPMC10853872 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature