Accepted for/Published in: JMIR Research Protocols
Date Submitted: Feb 11, 2024
Open Peer Review Period: Feb 12, 2024 - Apr 8, 2024
Date Accepted: Apr 10, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Evaluating Artificial Intelligence in Ophthalmology: A Protocol for Systematic Review and Meta-Analysis of AI Algorithms for Diabetic Retinopathy Detection
ABSTRACT
Background:
Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. The global burden is immense with a worldwide prevalence of 8.5%. Recently, advancements in artificial intelligence (AI) have demonstrated the potential to transform the landscape of ophthalmology with early detection and management of DR.
Objective:
This study seeks to provide an update and evaluate the accuracy and current diagnostic ability of AI in detecting DR versus ophthalmologists. Additionally, this review will highlight the potential of AI-integration to enhance DR screening, management, and disease progression.
Methods:
A systematic review of the current landscape of AI’s role in DR will be undertaken, guided by the PRISMA model. Relevant peer-reviewed articles published in English will be identified by searching the international databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. If upon systematic review completion it is determined there is sufficient data a meta-analysis will be performed.
Results:
Using selected search queries across multiple databases, we accumulated a total of 3494 studies regarding our topic of interest. 1588 were duplicates, leaving a total of 1906 unique research articles to review and analyze.
Conclusions:
This systematic review and meta-analysis protocol outlines a comprehensive evaluation of AI for diabetic retinopathy detection.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.