人工智能算法在糖尿病视网膜病变检测中的评估：系统评价和荟萃分析的方案。

Evaluation of Artificial Intelligence Algorithms for Diabetic Retinopathy Detection: Protocol for a Systematic Review and Meta-Analysis.

机构信息

Office of Medical Research, University of Nevada, Reno School of Medicine, Reno, NV, United States.

Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV, United States.

出版信息

JMIR Res Protoc. 2024 May 27;13:e57292. doi: 10.2196/57292.

DOI:10.2196/57292

PMID:38801771

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11165278/

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 (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model. Relevant peer-reviewed papers published in English will be identified by searching 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 that focus on specific comorbid conditions, nonimage-based applications of AI, or those lacking a direct comparison group or clear methodology will be excluded. Selected papers will be independently assessed for bias by 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accuracy Studies tool for systematic reviews. Upon systematic review completion, if it is determined that there are sufficient data, a meta-analysis will be performed. Data synthesis will use a quantitative model. Statistical software such as 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 3494 studies regarding our topic of interest, of which 1588 were duplicates, leaving 1906 unique research papers to review and analyze.

CONCLUSIONS

This systematic review and meta-analysis protocol outlines a comprehensive evaluation of AI for DR detection. This active study is anticipated to assess the current accuracy of AI methods in detecting DR.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57292.

摘要

背景

糖尿病视网膜病变（DR）是糖尿病最常见的并发症之一。全球负担巨大，患病率为 8.5%。人工智能（AI）的最新进展表明，它有可能通过早期检测和管理 DR 来改变眼科领域的格局。

目的

本研究旨在提供 AI 在检测 DR 方面的最新进展，并评估其与眼科医生相比的准确性和当前诊断能力。此外，本综述还将强调 AI 集成在增强 DR 筛查、管理和疾病进展方面的潜力。

方法

将根据 PRISMA（系统评价和荟萃分析的首选报告项目）模型对 AI 在 DR 中的作用的当前研究现状进行系统综述。通过搜索 4 个国际数据库：PubMed、Embase、CINAHL 和 Cochrane 对照试验中心注册库，确定发表在英文期刊上的相关同行评审论文。合格的研究将包括 2022 年或之后发表的评估 AI 在不同成年人群体的视网膜成像检测 DR 中的表现的随机对照试验、观察性研究和队列研究。将排除专注于特定合并症、非基于图像的 AI 应用或缺乏直接比较组或明确方法的研究。选定的论文将由 2 位评审作者（JS 和 DM）独立评估偏倚，使用诊断准确性研究的质量评估工具进行系统评价。完成系统评价后，如果确定有足够的数据，将进行荟萃分析。数据综合将使用定量模型。将使用 RevMan 和 STATA 等统计软件生成随机效应荟萃回归模型，以汇总选定研究的数据。