Evidence map›Paper›PMID 37383397›Full record

SynthesisFrontiers in endocrinology2023

Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.

Zhibin Wang, Zhaojin Li, Kunyue Li, Siyuan Mu, Xiaorui Zhou, Yu Di

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhibin WangDepartment of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, China.
Zhaojin LiDepartment of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, China.
Kunyue LiDepartment of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, China.
Siyuan MuDepartment of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, China.
Xiaorui ZhouDepartment of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, China.
Yu DiDepartment of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness. Materials and methods: A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm. Results: Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR. Conclusion: AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.

Indexed as

Diabetes MellitusDiabetic RetinopathyAlgorithmsArtificial IntelligenceHumansProspective StudiesSoftwareartificial intelligencediabetic retinopathydiagnostic accuracymeta-analysisprospective study

Identifiers

PMID37383397
PMCPMC10296189

What Socratic holds

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Registered trials

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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 Socratic graph.