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. 2024 Mar 18;17(3):408-419.
doi: 10.18240/ijo.2024.03.02. eCollection 2024.

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis

Nan-Nan Shi  1   2 , Jing Li  1   2 , Guang-Hui Liu  1   2 , Ming-Fang Cao  1   2
Affiliations

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis

Nan-Nan Shi et al. Int J Ophthalmol. .

Abstract

Aim: To quantify the performance of artificial intelligence (AI) in detecting glaucoma with spectral-domain optical coherence tomography (SD-OCT) images.

Methods: Electronic databases including PubMed, Embase, Scopus, ScienceDirect, ProQuest and Cochrane Library were searched before May 31, 2023 which adopted AI for glaucoma detection with SD-OCT images. All pieces of the literature were screened and extracted by two investigators. Meta-analysis, Meta-regression, subgroup, and publication of bias were conducted by Stata16.0. The risk of bias assessment was performed in Revman5.4 using the QUADAS-2 tool.

Results: Twenty studies and 51 models were selected for systematic review and Meta-analysis. The pooled sensitivity and specificity were 0.91 (95%CI: 0.86-0.94, I2=94.67%), 0.90 (95%CI: 0.87-0.92, I2=89.24%). The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 8.79 (95%CI: 6.93-11.15, I2=89.31%) and 0.11 (95%CI: 0.07-0.16, I2=95.25%). The pooled diagnostic odds ratio (DOR) and area under curve (AUC) were 83.58 (95%CI: 47.15-148.15, I2=100%) and 0.95 (95%CI: 0.93-0.97). There was no threshold effect (Spearman correlation coefficient=0.22, P>0.05).

Conclusion: There is a high accuracy for the detection of glaucoma with AI with SD-OCT images. The application of AI-based algorithms allows together with "doctor+artificial intelligence" to improve the diagnosis of glaucoma.

Keywords: Meta-analysis; artificial intelligence; glaucoma; spectral-domain optical coherence tomography.

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Conflict of interest statement

Conflicts of Interest: Shi NN, None; Li J, None; Liu GH, None; Cao MF, None.

Figures

Figure 1
Figure 1. Study selection flow diagram.
Figure 2
Figure 2. Risk of bias assessment of included studies via QUADAS-2 tool.
Figure 3
Figure 3. The forest plot of the pooled sensitivity and specificity.
Figure 4
Figure 4. The forest plot of the PLR and NLR
PLR: Positive likelihood ratio; NLR: Negative likelihood ratio.
Figure 5
Figure 5. The forest plot of the DOR and SROC curve
DOR: Diagnostic odds ratio; SROC: Summary receiver operator characteristic.
Figure 6
Figure 6. Deek's funnel plot of each model.
Figure 7
Figure 7. Sensitivity analysis of each model.
See this image and copyright information in PMC

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