ArticleDiagnostics (Basel, Switzerland)2022
Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Accuracy of AI-based raman spectroscopy in the diagnosis of gastric cancer: a systematic review and meta-analysis.Lasers in medical science · 2026Pooled it
- AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.Eye (London, England) · 2025Pooled it
- Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.Ophthalmology science · 2026Article
- Multiple attention based deep multimodal fusion network for glaucoma and neurodegenerative disease diagnosis.Scientific reports · 2026Article
- Early Detection of Dementia Through Spectralis Optical Coherence Tomography in a Taiwanese Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Spectralis Optical Coherence Tomography for Evaluating Ocular Hypertensive and Glaucoma Suspect Eyes: Real-World Data from Taiwan.Diagnostics (Basel, Switzerland) · 2025Article
- Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.Current eye research · 2025Article
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- A Survey on Optical Coherence Tomography-Technology and Application.Bioengineering (Basel, Switzerland) · 2025Review
- Amygdala Connectivity Alterations in High Myopia: A Resting-State fMRI Study with SVM-Based Classification.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Macular OCT's Proficiency in Identifying Retrochiasmal Visual Pathway Lesions in Multiple Sclerosis-A Pilot Study.Diagnostics (Basel, Switzerland) · 2024Article
- Glaucoma detection model by exploiting multi-region and multi-scan-pattern OCT images with dynamical region score.Biomedical optics express · 2024Article
- Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.International journal of ophthalmology · 2024Article
- Ocular and inflammatory markers associated with Gulf War illness symptoms.Scientific reports · 2023Article
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Spectralis optical coherence tomography (OCT) provided more detailed parameters in the peripapillary and macular areas among the OCT machines, but it is not easy to understand the enormous information (114 features) generated from Spectralis OCT in glaucoma assessment. Machine learning methodology has been well-applied in glaucoma detection in recent years and has the ability to process a large amount of information at once. Here we aimed to analyze the diagnostic capability of Spectralis OCT parameters on glaucoma detection using Support Vector Machine (SVM) classification method in our population. Our results showed that applying all OCT features with the SVM method had good capability in the detection of glaucomatous eyes (area under curve (AUC) = 0.82), as well as discriminating normal eyes from early, moderate, or severe glaucomatous eyes (AUC = 0.78, 0.89, and 0.93, respectively). Apart from using all OCT features, the minimum rim width (MRW) may be good feature groups to discriminate early glaucomatous from normal eyes (AUC = 0.78). The combination of peripapillary and macular parameters, including MRW_temporal inferior (TI), MRW_global (G), ganglion cell layer (GCL)_outer temporal (T2), GCL_inner inferior (I1), peripapillary nerve fiber layer thickness (ppNFLT)_temporal superior (TS), and GCL_inner temporal (T1), provided better results (AUC = 0.84). This study showed promise in glaucoma management in the Taiwanese population. However, further validation study is needed to test the performance of our proposed model in the real world.
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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.