Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.

ABSTRACT:

Background

To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA).

Methods

In this cross-sectional observational study, clinical data and OCTA parameters from 203 diabetic patients (203 eye) were used to establish the ML models, and those from 169 diabetic patients (169 eye) were used for independent external validation. The random forest, gradient boosting machine (GBM), deep learning and logistic regression algorithms were used to identify the presence of DR, referable DR (RDR) and vision-threatening DR (VTDR). Four different variable patterns based on clinical data and OCTA variables were examined. The algorithms' performance were evaluated using

SUBMITTER: Li X 

PROVIDER: S-EPMC11427469 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature