A comparative analysis of pretrained and transfer-learning model for automatic diagnosis of glaucoma

Elakkiya, B; Saraniya, O.

doi:10.1109/icoac48765.2019.246835

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…We identified a total of 79 studies for qualitative synthesis comprising private data sets (49), publicly available datasets (26), and mixed (public and private) data sets (4). Predominantly, the data were contributed from India (32%) and Spain (24%), followed by the United States (13%), Korea (11%), and others (Table 1).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice

Chaurasia

Greatbatch

Hewitt

2022

Journal of Glaucoma

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Purpose: Artificial intelligence (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 metaanalysis 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 optical coherence tomography (OCT) images. A bivariate random-effects model was used to determine the summary estimates for diagnostic outcomes. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy (PRISMA-DTA) extension was followed, and the Quality Assessment of Diagnostic Accuracy Studies-2 (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 area under receiver operating characteristic curve across all studies for glaucoma detection was 96.3%, with a sensitivity of 92.0% (95% confidence interval: 89.0-94.0) and specificity of 94.0% (95% confidence interval: 92.0-95.0). The pooled area under receiver operating characteristic curve on fundus and OCT images was 96.2% and 96.0%, respectively. Mixed data set 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.

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Section: Discussionmentioning

confidence: 99%