Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.

ABSTRACT:

Background

Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine learning (ML)-based automated DR diagnosis has gained attention due to recent approval of IDx-DR, performance of this tool has not been examined systematically, and the best ML technique for use in a real-world setting has not been discussed.

Objective

The aim of this study was to systematically examine the overall diagnostic accuracy of ML in diagnosing DR of different categories based on color fundus photographs and to determine the state-of-the-art ML approach.

Methods

Published studies in PubMed and EMBASE were searched from inception to June 2020. Studies were screened for relevant outco

SUBMITTER: Wu JH 

PROVIDER: S-EPMC8406115 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature