Analysis of OCT Images to Optimize Glaucoma Diagnosis

Akter, Nahida; Phu, Jack; Perry, Stuart; Fletcher, John; Kalloniatis, Michael; Roy, Maitreyee

doi:10.1364/isa.2019.ith2b.2

Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP) 2019

DOI: 10.1364/isa.2019.ith2b.2

|View full text |Cite

Analysis of OCT Images to Optimize Glaucoma Diagnosis

Nahida Akter

Jack Phu

Stuart Perry

et al.

Abstract: In this paper, data from OCT images are extracted, statistically analyzed and further an image processing task has been performed on optic nerve head images to optimize features used in the diagnosis of glaucoma.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

Akter

Fletcher

Perry

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.

show abstract

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

Akter

Fletcher

Perry

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Analysis of OCT Images to Optimize Glaucoma Diagnosis

Abstract: In this paper, data from OCT images are extracted, statistically analyzed and further an image processing task has been performed on optic nerve head images to optimize features used in the diagnosis of glaucoma.

Cited by 1 publication

References 4 publications

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

Contact Info

Product

Resources

About