A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study.

ABSTRACT:

Background

The aim of our research was to prospectively explore the clinical value of a deep learning algorithm (DLA) to detect referable diabetic retinopathy (DR) in different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, glycosylated hemoglobin (HbA1c), diabetes duration, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) at a real-world diabetes center in China.

Methods

A total of 1147 diabetic patients from Shanghai General Hospital were recruited from October 2018 to August 2019. Retinal fundus images were graded by the DLA, and the detection of referable DR (moderate nonproliferative DR or worse) was compared with a reference standard generated by one certified retinal specialist with more than 12 years

SUBMITTER: Li N 

PROVIDER: S-EPMC9060020 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature