摘要
一位新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑-人工智能的新研究是一篇报道的主题。根据NewsRx Ed Itors在内华达州里诺市的新闻报道，研究表明：“糖尿病性视网膜病变(DR)是糖尿病最常见的并发症之一，全球负担巨大，全球发病率为8.5%。”我们的新闻记者从N Evada大学的研究中获得了一句话，"人工智能(AI)的最新进展证明了通过对DR的早期检测和管理来改变眼科景观的潜力。本研究旨在提供一个更新和评估人工智能在检测DR与眼科医生S方面的准确性和当前诊断能力。此外，本综述将强调人工智能整合到人类DR筛查、管理和疾病进展中的潜力。将在PRIS MA(系统回顾和荟萃分析首选报告项目)模型的指导下，对人工智能在DR中的作用进行系统回顾。以英文发表的相关同行评议论文将由Searchin G 4国际数据库确定：PubMed,Embase,CINAHL,符合条件的研究将包括2022年或之后发表的随机对照试验、观察性研究和队列研究，评估AI在不同成人人群视网膜成像检测DR方面的表现。或那些缺乏直接比较组或明确方法的论文将被排除在外。选择的论文将使用系统回顾的诊断准确性质量评估工具对2位综述作者(JS和DM)的偏倚进行独立评估。系统回顾完成后，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Rev Man和STATA等统计软件产生随机效应元回归模型，以汇集来自选定研究的数据。使用跨多个D数据库的选定搜索查询，我们积累了3494项关于我们感兴趣主题的研究，其中1588项是重复的。留下1906篇独特的研究论文进行回顾和分析。本系统回顾和荟萃分析方案概述了用于DR检测的人工智能的全面评估。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting out of Reno, Nevada, by NewsRx ed itors, research stated, “Diabetic retinopathy (DR) is one of the most common com plications of diabetes mellitus. The global burden is immense with a worldwide p revalence of 8.5%.” Our news journalists obtained a quote from the research from the University of N evada, “Recent advancements in artificial intelligence (AI) have demonstrated th e potential to transform the landscape of ophthalmology with earlier detection a nd management of DR. This study seeks to provide an update and evaluate the accu racy and current diagnostic ability of AI in detecting DR versus ophthalmologist s. Additionally, this review will highlight the potential of AI integration to e nhance DR screening, management, and disease progression. A systematic review of the current landscape of AI’s role in DR will be undertaken, guided by the PRIS MA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model. R elevant peer-reviewed papers published in English will be identified by searchin g 4 international databases: PubMed, Embase, CINAHL, and the Cochrane Central Re gister of Controlled Trials. Eligible studies will include randomized controlled trials, observational studies, and cohort studies published on or after 2022 th at 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 methodo logy will be excluded. Selected papers will be independently assessed for bias b y 2 review authors (JS and DM) using the Quality Assessment of Diagnostic Accura cy 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 Rev Man and STATA will be used to produce a random-effects meta-regression model to pool data from selected studies. Using selected search queries across multiple d atabases, we accumulated 3494 studies regarding our topic of interest, of which 1588 were duplicates, leaving 1906 unique research papers to review and analyze. This systematic review and meta-analysis protocol outlines a comprehensive eval uation of AI for DR detection.”
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