首页|University of Nevada Reports Findings in Artificial Intelligence (Evaluation of Artificial Intelligence Algorithms for Diabetic Retinopathy Detection: Protocol for a Systematic Review and Meta-Analysis)
University of Nevada Reports Findings in Artificial Intelligence (Evaluation of Artificial Intelligence Algorithms for Diabetic Retinopathy Detection: Protocol for a Systematic Review and Meta-Analysis)
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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.”
RenoNevadaUnited StatesNorth and C entral AmericaAlgorithmsArtificial IntelligenceClinical ResearchClinical Trials and StudiesDiabetic AngiopathiesDiabetic RetinopathyEmerging Techn ologiesEye Diseases and ConditionsHealth and MedicineMachine LearningNut ritional and Metabolic Diseases and ConditionsOphthalmologyRetinal Diseases and ConditionsRetinopathyVascular Diseases and Conditions
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