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Abstract #99135 Published in IGR 22-4
Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice
Chaurasia AK; Greatbatch CJ; Hewitt AWJournal of Glaucoma 2022; 31: 285-299
PURPOSE: AI has been shown as a diagnostic tool for glaucoma detection through imaging modalities. However, these tools are yet to be deployed into clinical practice. This meta-analysis determined overall AI performance for glaucoma diagnosis and identified potential factors affecting their implementation. METHODS: We searched databases (Embase, Medline, Web of Science, and Scopus) for studies that developed or investigated the use of AI for glaucoma detection using fundus and OCT images. A bivariate random-effects model was used to determine the summary estimates for diagnostic outcomes. The PRISMA-DTA extension was followed, and the QUADAS-2 tool was used for bias and applicability assessment. RESULTS: Seventy-nine articles met inclusion criteria, with a subset of 66 containing adequate data for quantitative analysis. The pooled AUC across all studies for glaucoma detection was 96.3%, with a sensitivity of 92.0% (95% CI: 89.0-94.0) and specificity of 94.0% (95% CI: 92.0-95.0). The pooled AUC on fundus and OCT images was 96.2% and 96.0%, respectively. Mixed dataset and external data validation had unsatisfactory diagnostic outcomes. CONCLUSION: Although AI has the potential to revolutionize glaucoma care, this meta-analysis highlights that before such algorithms can be implemented into clinical care, a number of issues need to be addressed. With substantial heterogeneity across studies, many factors were found to affect the diagnostic performance. We recommend implementing a standard diagnostic protocol for grading, implementing external data validation, and analysis across different ethnicity groups.
Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Australia Centre for Eye Research Australia, University of Melbourne, Australia.
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