Abstract
Background/Objective
Pathologic myopia (PM) is a major cause of severe visual impairment and blindness, and current applications of artificial intelligence (AI) have covered the diagnosis and classification of PM. This meta-analysis and systematic review aimed to evaluate the overall performance of AI-based models in detecting PM and related complications.
Methods
We searched PubMed, Scopus, Embase, Web of Science and IEEE Xplore for eligible studies before Dec 20, 2022. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We calculated the pooled sensitivity (SEN), specificity (SPE) and the summary area under the curve (AUC) using a random effects model, to evaluate the performance of AI in the detection of PM based on fundus or optical coherence tomography (OCT) images.
Results
22 studies were included in the systematic review, and 14 of them were included in the quantitative analysis. Of all included studies, SEN and SPE ranged from 80.0% to 98.7% and from 79.5% to 100.0% for PM detection, respectively. For the detection of PM, the summary AUC was 0.99 (95% confidence interval (CI) 0.97 to 0.99), and the pooled SEN and SPE were 0.95 (95% CI 0.92 to 0.96) and 0.97 (95% CI: 0.94 to 0.98), respectively. For the detection of PM-related choroid neovascularization (CNV), the summary AUC was 0.99 (95% CI: 0.97 to 0.99).
Conclusion
Our review demonstrated the excellent performance of current AI algorithms in detecting PM and related complications based on fundus and OCT images.
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Data availability
Data are available from the corresponding author on reasonable request.
Change history
13 December 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41433-023-02888-z
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Funding
This study was supported by National High Level Hospital Clinical Research Funding (BJ-2022-104).
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YZ was responsible for the conceptualization of the research topic, designing and writing the protocol, conducting the database, writing, and editing the paper. YL was responsible for analyzing the data using statistical software and drawing the figures. JNW, HL, and JRZ were responsible for the screening of the studies, conducting the risk of bias assessment, curating the data. JL was responsible for the conceptualization of the research topic. XBY was responsible for the validation of results, review and editing the paper.
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The original online version of this article was revised: the statement in the Funding information section was incorrectly given as ‘This study was supported by Medical and Engineering Combination Project of Beijing Hospital (BJ-2022-104).’ The correct funding information should read as follows: ‘This study was supported by National High Level Hospital Clinical Research Funding (BJ-2022-104).’
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Zhang, Y., Li, Y., Liu, J. et al. Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis. Eye 37, 3565–3573 (2023). https://doi.org/10.1038/s41433-023-02551-7
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DOI: https://doi.org/10.1038/s41433-023-02551-7
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