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)
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%. Recent advancements in artificial intelligence (AI) have demonstrated the potential to transform the landscape of ophthalmology with earlier 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 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Eligible studies will include randomized controlled-trials, observational studies, and cohort studies published on or after 2022 that evaluate AI’s performance in retinal imaging detection of DR in diverse adult populations. Studies which focus on specific comorbid conditions, non-image-based applications of AI, or those lacking a direct comparison group or clear methodology will be excluded. Selected articles will be independently assessed for bias by 2 review authors utilizing the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for systematic reviews. If upon systematic review completion it is determined there is sufficient data a meta-analysis will be performed. Data synthesis will employ a quantitative model. The statistical software RevMan and STATA, will be used to produce a random-effects meta-regression model to pool data from selected studies.
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:
his systematic review and meta-analysis protocol outlines a comprehensive evaluation of AI for diabetic retinopathy detection. This active study is anticipated to assess the current accuracy of AI methods in detecting DR.
Citation
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Copyright
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