ArticleInternational journal of ophthalmology2020
Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis.
Article in International journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Artificial intelligence for glaucoma.The Cochrane database of systematic reviews · 2025Pooled it
- Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.Eye (London, England) · 2022Pooled it
- Health care cost and benefits of artificial intelligence-assisted population-based glaucoma screening for the elderly in remote areas of China: a cost-offset analysis.BMC public health · 2021Pooled it
- GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.Scientific reports · 2025Article
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening.Medicina (Kaunas, Lithuania) · 2024Review
- Artificial intelligence assisted pterygium diagnosis: current status and perspectives.International journal of ophthalmology · 2023Article
- Corneal Hysteresis, Intraocular Pressure, and Progression of Glaucoma: Time for a "Hyst-Oric" Change in Clinical Practice?Journal of clinical medicine · 2022Review
- Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study.NPJ digital medicine · 2022Article
- Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images.Multimedia tools and applications · 2022Article
- Glaucoma detection in Latino population through OCT's RNFL thickness map using transfer learning.International ophthalmology · 2021Article
- Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population.Ophthalmology scienceArticle
- Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo compare the effectiveness of two well described machine learning modalities, ocular coherence tomography (OCT) and fundal photography, in terms of diagnostic accuracy in the screening and diagnosis of glaucoma.
methodsA systematic search of Embase and PubMed databases was undertaken up to 1
resultsA total of 23 studies were deemed suitable for inclusion in the Meta-analysis. This included 10 papers from the OCT cohort and 13 from the fundal photos cohort. Random effects Meta-analysis gave a pooled AUROC of 0.957 (95%CI=0.917 to 0.997) for fundal photos and 0.923 (95%CI=0.889 to 0.957) for the OCT cohort. The slightly higher accuracy of fundal photos methods is likely attributable to the much larger database of images used to train the models (59 788
conclusionNo demonstrable difference is shown between the diagnostic accuracy of the two modalities. The ease of access and lower cost associated with fundal photo acquisition make that the more appealing option in terms of screening on a global scale, however further studies need to be undertaken, owing largely to the poor study quality associated with the fundal photography cohort.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.