The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy.

ABSTRACT:

Purpose

To evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR).

Materials and methods

The prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity and specificity) and secondary evaluation index like positive predictive values (PPV), negative predictive values (NPV), etc., were calculated to evaluate the performance of EyeWisdom V1.

Results

A total of 1,089 fundus images from 630 patients were included, with a mean age of

SUBMITTER: Zhang WF 

PROVIDER: S-EPMC9148973 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature