ArticleTranslational vision science & technology2021
Combining Structural and Vascular Parameters to Discriminate Among Glaucoma Patients, Glaucoma Suspects, and Healthy Subjects.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma.American journal of ophthalmology · 2023Article
- Comparison of Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Thickness Values Using Spectral-Domain and Swept-Source OCT.Translational vision science & technology · 2022Article
- Degeneration of retina-brain components and connections in glaucoma: Disease causation and treatment options for eyesight preservation.Current research in neurobiology · 2022Review
- Multipressure Dial Goggle Effects on Circumpapillary Structure and Microvasculature in Glaucoma Patients.Ophthalmology. GlaucomaArticle
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
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Authors and funding
11 authors.
Funding
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Abstract
Purpose: Compare the ability of peripapillary and macular structural parameters, vascular parameters, and their integration to discriminate among glaucoma, suspected glaucoma (GS), and healthy controls (HCs). Methods: In this study, 196 eyes of 119 patients with glaucoma (n = 81), patients with GS (n = 48), and HCs (n = 67) underwent optical coherence tomography (OCT) and OCT angiography to measure peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell-inner plexiform layer (mGCIPL) thicknesses, radial peripapillary capillary perfusion density (RPC-PD), and macular GCIPL perfusion density (GCIPL-PD). Parameters were integrated regionally with logistic regression and globally with machine learning algorithms. Diagnostic performances were evaluated with area under the receiver operating characteristic (AUROC) curves. Results: Patients with glaucoma had mild to moderate damage (median, -3.3 dB; interquartile range, -6.5 to -1.4). In discriminating between patients with glaucoma and the HCs, pRNFL thickness had higher AUROC curve values than RPC-PD for average (0.87 vs. 0.62; P < 0.001), superior (0.86 vs. 0.54; P < 0.001), inferior (0.90 vs. 0.71; P < 0.001), and temporal (0.65 vs. 0.51; P = 0.02) quadrants. mGCIPL thickness had higher AUROC curve values than GCIPL-PD for average (0.84 vs. 0.68; P < 0.001), superotemporal (0.76 vs. 0.65; P = 0.016), superior (0.72 vs. 0.57; P = 0.004), superonasal (0.70 vs. 0.56; P = 0.01), inferotemporal (0.90 vs. 0.72; P < 0.001), inferior (0.87 vs. 0.69; P < 0.001), and inferonasal (0.78 vs. 0.65, P = 0.012) sectors. All structural multisector indices had higher diagnostic ability than vascular ones (P < 0.001). Combined structural-vascular indices did not outperform structural indices. Similar results were found to discriminate glaucoma from GS. Conclusions: Combining structural and vascular parameters in a structural-vascular index does not improve diagnostic ability over structural parameters alone. Translational Relevance: OCT angiography does not add additional benefit to structural OCT in early to moderate glaucoma diagnosis.
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