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Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.
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, I 2 =94.67%), 0.90 (95%CI: 0.87-0.92, I 2 =89.24%). The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 8.79 (95%CI: 6.93-11.15, I 2 =89.31%) and 0.11 (95%CI: 0.07-0.16, I 2 =95.25%). The pooled diagnostic odds ratio (DOR) and area under curve (AUC) were 83.58 (95%CI: 47.15-148.15, I 2 =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.
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, I 2 =94.67%), 0.90 (95%CI: 0.87-0.92, I 2 =89.24%). The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 8.79 (95%CI: 6.93-11.15, I 2 =89.31%) and 0.11 (95%CI: 0.07-0.16, I 2 =95.25%). The pooled diagnostic odds ratio (DOR) and area under curve (AUC) were 83.58 (95%CI: 47.15-148.15, I 2 =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.
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