Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers
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Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes.Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.Heidelberg retina tomograph II findings of Acanthamoeba keratitis.Optic nerve head and retinal nerve fiber layer analysis: a report by the American Academy of Ophthalmology.Combining nerve fiber layer parameters to optimize glaucoma diagnosis with optical coherence tomography.Diagnostic tools for glaucoma detection and management.Morphometric analysis and classification of glaucomatous optic neuropathy using radial polynomials.Detecting abnormality in optic nerve head images using a feature extraction analysis.Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiersHeidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection.Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurementsEvaluating objective and subjective quantitative parameters at the initial visit to predict future glaucomatous visual field progressionPattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART.Correlation between optic nerve head parameters, RNFL, and CCT in patients with bilateral pseudoexfoliation using HRT-III.Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiersCan Visual Field Progression be Predicted by Confocal Scanning Laser Ophthalmoscopic Imaging of the Optic Nerve Head in Glaucoma? (An American Ophthalmological Society Thesis).Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study.Machine learning classifiers detect subtle field defects in eyes of HIV individuals.Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.Analysis with support vector machine shows HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyesAssessing visual field clustering schemes using machine learning classifiers in standard perimetry.Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography.Diagnosing breast masses in digital mammography using feature selection and ensemble methods.Utility of Heidelberg retinal tomography as a screening tool for analyzing retinal nerve fiber layer defects.Influence of refractive condition on retinal vasculature complexity in younger subjects.
P2860
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P2860
Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers
description
2004 nî lūn-bûn
@nan
2004 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
Heidelberg retina tomograph me ...... y machine learning classifiers
@ast
Heidelberg retina tomograph me ...... y machine learning classifiers
@en
Heidelberg retina tomograph me ...... y machine learning classifiers
@nl
type
label
Heidelberg retina tomograph me ...... y machine learning classifiers
@ast
Heidelberg retina tomograph me ...... y machine learning classifiers
@en
Heidelberg retina tomograph me ...... y machine learning classifiers
@nl
prefLabel
Heidelberg retina tomograph me ...... y machine learning classifiers
@ast
Heidelberg retina tomograph me ...... y machine learning classifiers
@en
Heidelberg retina tomograph me ...... y machine learning classifiers
@nl
P2093
P2860
P50
P356
P1476
Heidelberg retina tomograph me ...... y machine learning classifiers
@en
P2093
Jicuang Hao
Kwokleung Chan
Michael H Goldbaum
Te-Won Lee
P2860
P304
P356
10.1167/IOVS.04-0202
P407
P577
2004-09-01T00:00:00Z