![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
|
PDF (Original Article)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB | |
|
PDF (Supplement)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
3MB |
| Item Type: | Article |
|---|---|
| Title: | Central macular topographic and volumetric measures: new biomarkers for detection of glaucoma |
| Creators Name: | Mohammadzadeh, V. and Cheng, M. and Zadeh, S.H. and Edalati, K. and Yalzadeh, D. and Caprioli, J. and Yadav, S. and Kadas, E.M. and Brandt, A.U. and Nouri-Mahdavi, K. |
| Abstract: | PURPOSE: To test the hypothesis that newly developed shape measures using optical coherence tomography (OCT) macular volume scans can discriminate patients with perimetric glaucoma from healthy subjects. METHODS: OCT structural measures defining macular topography and volume were recently developed based on cubic Bézier curves. We exported macular volume scans from 135 eyes with glaucoma (133 patients) and 155 healthy eyes (85 subjects) and estimated global and quadrant-based measures. The best subset of measures to predict glaucoma was explored with a gradient boost model (GBM) with subsequent logistic regression. Accuracy and area under receiver operating curves (AUC) were the primary metrics. In addition, we separately investigated model performance in 66 eyes with mild glaucoma (mean deviation ≥ -6 dB). RESULTS: Average (±SD) 24-2 mean deviation was -8.2 (±6.1) dB in eyes with glaucoma. The main predictive measures for glaucoma were temporal inferior rim height, nasal inferior pit volume, and temporal inferior pit depth. Lower values for these measures predicted higher risk of glaucoma. Sensitivity, specificity, and AUC for discriminating between healthy and glaucoma eyes were 81.5% (95% CI = 76.6-91.9%), 89.7% (95% CI = 78.7-94.2%), and 0.915 (95% CI = 0.882-0.948), respectively. Corresponding metrics for mild glaucoma were 84.8% (95% CI = 72.1%-95.5%), 85.8% (95% CI = 87.1%-97.4%), and 0.913 (95% CI = 0.867-0.958), respectively. CONCLUSIONS: Novel macular shape biomarkers detect early glaucoma with clinically relevant performance. Such biomarkers do not depend on intraretinal segmentation accuracy and may be helpful in eyes with suboptimal macular segmentation. TRANSLATIONAL RELEVANCE: Macular shape biomarkers provide valuable information for detection of early glaucoma and may provide additional information beyond thickness measurements. |
| Keywords: | Optical Coherence Tomography (OCT), Macula, Foveal Shape, Topography, Volume, Deep Learning, Gradient Boost Model |
| Source: | Translational Vision Science & Technology |
| ISSN: | 2164-2591 |
| Publisher: | Association for Research in Vision and Opthalmology |
| Volume: | 11 |
| Number: | 7 |
| Page Range: | 25 |
| Date: | July 2022 |
| Official Publication: | https://doi.org/10.1167/tvst.11.7.25 |
| PubMed: | View item in PubMed |
Repository Staff Only: item control page
Download Statistics
Download Statistics
