Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.

ABSTRACT:

Objectives

To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening.

Methods

We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively.

Results

Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most s

SUBMITTER: Zhelev Z 

PROVIDER: S-EPMC10399100 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature