SynthesisThe Cochrane database of systematic reviews2025
Artificial intelligence for glaucoma.
Synthesis in The Cochrane database of systematic reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the accuracy of artificial intelligence (AI) algorithms as a diagnostic tool for glaucoma compared with human graders in a community or secondary care setting. Secondary objectives To compare the performance of different AI algorithms in the diagnosis of glaucoma To explore other potential causes of heterogeneity in the diagnostic performance compared with human graders, using subgroup analysis of the following characteristics: Clinical setting in which the test is used (the general population in the community versus people referred to secondary care); Study design (studies enroling consecutive participants in the same setting versus multicentre registries and public databases); Characteristics of the population (age according to quartiles, sex, symptomatic versus asymptomatic), when sufficient data are available; Prevalence of glaucoma in the training and test sets (< 5% versus ≥ 5%), since sensitivity and specificity may depend on disease prevalence [17]; Severity of glaucoma; Core AI method used (neural networks, random forests, support vector machines, or others); Size of the dataset from which performance data were collected (< 1000 versus ≥ 1000 total unique participants); Modalities of input data for the AI algorithms (imaging data, visual fields, clinical parameters, or any combination), particularly as their accessibility and affordability may vary across different settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.