ArticlePloS one2013
Three-dimensional spectral-domain optical coherence tomography data analysis for glaucoma detection.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Multiple attention based deep multimodal fusion network for glaucoma and neurodegenerative disease diagnosis.Scientific reports · 2026Article
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.Eye (London, England) · 2024Review
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
- The utilization of artificial intelligence in glaucoma: diagnosis versus screening.Frontiers in ophthalmology · 2024Review
- On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.Bioengineering (Basel, Switzerland) · 2023Review
- Depth-resolved extraction of optical attenuation for glaucoma assessment in clinical settings: a pilot study.Biomedical optics express · 2022Article
- Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.Translational vision science & technology · 2020Article
- Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis.International journal of ophthalmology · 2020Article
- Joint retina segmentation and classification for early glaucoma diagnosis.Biomedical optics express · 2019Article
- Using Small Samples to Evaluate Normative Reference Ranges for Retinal Imaging Measures.Optometry and vision science : official publication of the American Academy of Optometry · 2019Article
- Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.Eye (London, England) · 2019Review
- Effect of Refractive Correction Error on Retinal Nerve Fiber Layer Thickness: A Spectralis Optical Coherence Tomography Study.Medical science monitor : international medical journal of experimental and clinical research · 2016Article
- Retinal status analysis method based on feature extraction and quantitative grading in OCT images.Biomedical engineering online · 2016Article
- Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.Artificial intelligence in medicine · 2015Article
- Glaucoma progression detection using nonlocal Markov random field prior.Journal of medical imaging (Bellingham, Wash.) · 2014Article
- Clinical Applications of Artificial Intelligence in Glaucoma.Journal of ophthalmic & vision researchReview
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeTo develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes.
methods192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements.
resultsThe super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes.
conclusionsA novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage.
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