Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study.

ABSTRACT:

Objective

To develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection.

Design

This is a multicentre platform development study based on retrospective, cross-sectional data sets. Images were labelled by two-level certificated graders as the ground truth. According to the UK DR screening guideline, a DL model based on colour retinal images with five-dimensional classifiers, namely image quality, retinopathy, maculopathy gradability, maculopathy and photocoagulation, was developed. Referable decisions were generated by integrating the output of all classifiers and reported at the image, eye and patient level. The performance of the DL was compared with DR experts.

Setting

DR screening program

SUBMITTER: Zhang G 

PROVIDER: S-EPMC9341185 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature