Global Search

Search articles, concepts, and chapters

J GlaucomaMarch 20243 citations

Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening.

Beniz Luiz Arthur F, Campos Veronica P, Medeiros Felipe A


AI Summary

OCT is more accurate but costly, while disc photos are cheaper but subjective. AI integration, especially with objective OCT data, could optimize both for effective glaucoma screening.

Abstract

Précis: Optical coherence tomography (OCT) and optic disc photography present valuable but distinct capabilities for glaucoma screening.

Objective

This review article examines the strengths and limitations of OCT and optic disc photography in glaucoma screening.

Methods

A comprehensive literature review was conducted, focusing on the accuracy, feasibility, cost-effectiveness, and technological advancements in OCT and optic disc photography for glaucoma screening.

Results

OCT is highly accurate and reproducible but faces limitations due to its cost and less portable nature, making widespread screening challenging. In contrast, optic disc photos are more accessible and cost-effective but are hindered by subjective interpretation and inconsistent grading reliability. A critical challenge in glaucoma screening is achieving a high PPV, particularly given the low prevalence of the disease, which can lead to a significant number of false positives. The advent of artificial intelligence (AI) and deep learning models shows potential in improving the diagnostic accuracy of optic disc photos by automating the detection of glaucomatous optic neuropathy and reducing subjectivity. However, the effectiveness of these AI models hinges on the quality of training data. Using subjective gradings as training data, will carry the limitations of human assessment into the AI system, leading to potential inaccuracies. Conversely, training AI models using objective data from OCT, such as retinal nerve fiber layer thickness, may offer a promising direction.

Conclusion

Both OCT and optic disc photography present valuable but distinct capabilities for glaucoma screening. An approach integrating AI technology might be key in optimizing these methods for effective, large-scale screening programs.


MeSH Terms

HumansTomography, Optical CoherenceOptic DiskPhotographyGlaucomaReproducibility of ResultsOptic Nerve DiseasesNerve FibersRetinal Ganglion Cells

Key Concepts6

Optical coherence tomography (OCT) is highly accurate and reproducible for glaucoma screening but faces limitations due to its cost and less portable nature, making widespread screening challenging.

DiagnosisReviewComprehensive literature reviewn=Not applicableCh1Ch5Ch10

Optic disc photos are more accessible and cost-effective for glaucoma screening but are hindered by subjective interpretation and inconsistent grading reliability.

DiagnosisReviewComprehensive literature reviewn=Not applicableCh1Ch5Ch10

A critical challenge in glaucoma screening is achieving a high positive predictive value (PPV), particularly given the low prevalence of the disease, which can lead to a significant number of false positives.

DiagnosisReviewComprehensive literature reviewn=Not applicableCh1Ch10

The advent of artificial intelligence (AI) and deep learning models shows potential in improving the diagnostic accuracy of optic disc photos for glaucoma screening by automating the detection of glaucomatous optic neuropathy and reducing subjectivity.

DiagnosisReviewComprehensive literature reviewn=Not applicableCh1Ch5Ch10

The effectiveness of artificial intelligence (AI) models for glaucoma screening hinges on the quality of training data; using subjective gradings as training data will carry the limitations of human assessment into the AI system, leading to potential inaccuracies.

MethodologyReviewComprehensive literature reviewn=Not applicableCh1Ch10

Training artificial intelligence (AI) models for glaucoma screening using objective data from optical coherence tomography (OCT), such as retinal nerve fiber layer thickness, may offer a promising direction for improving diagnostic accuracy.

MethodologyReviewComprehensive literature reviewn=Not applicableCh1Ch5Ch10

Is this article assigned to the wrong chapter(s)? Let us know.