Comparison of shape-based analysis of retinal nerve fiber layer data obtained From OCT and GDx-VCC

Optical coherence tomography for glaucoma diagnosis: An evidence based meta-analysis

Purpose

Early detection, monitoring and understanding of changes in the retina are central to the diagnosis of glaucomatous optic neuropathy, and vital to reduce visual loss from this progressive condition. The main objective of this investigation was to compare glaucoma diagnostic accuracy of commercially available optical coherence tomography (OCT) devices (Zeiss Stratus, Zeiss Cirrus, Heidelberg Spectralis and Optovue RTVue, and Topcon 3D-OCT).

Patients

16,104 glaucomatous and 11,543 normal eyes reported in 150 studies.

Methods

Between Jan. 2017 and Feb 2017, MEDLINE^®, EMBASE^®, CINAHL^®, Cochrane Library^®, Web of Science^®, and BIOSIS^® were searched for studies assessing glaucoma diagnostic accuracy of the aforementioned OCT devices. Meta-analysis was performed pooling area under the receiver operating characteristic curve (AUROC) estimates for all devices, stratified by OCT type (RNFL, macula), and area imaged.

Results

150 studies with 16,104 glaucomatous and 11,543 normal control eyes were included. Key findings: AUROC of glaucoma diagnosis for RNFL average for all glaucoma patients was 0.897 (0.887–0.906, n = 16,782 patient eyes), for macula ganglion cell complex (GCC) was 0.885 (0.869–0.901, n = 4841 eyes), for macula ganglion cell inner plexiform layer (GCIPL) was 0.858 (0.835–0.880, n = 4211 eyes), and for total macular thickness was 0.795 (0.754–0.834, n = 1063 eyes).

Conclusion

The classification capability was similar across all 5 OCT devices. More diagnostically favorable AUROCs were demonstrated in patients with increased glaucoma severity. Diagnostic accuracy of RNFL and segmented macular regions (GCIPL, GCC) scans were similar and higher than total macular thickness. This study provides a synthesis of contemporary evidence with features of robust inclusion criteria and large sample size. These findings may provide guidance to clinicians when navigating this rapidly evolving diagnostic area characterized by numerous options.

[Variability and reproducibility of 3 methods for measuring the thickness of the nerve fiber layer]

OBJECTIVE

To estimate the variability and reproducibility of confocal tomography (HRT), scanning laser polarimetry (GDx) and optical coherence tomography (OCT-Cirrus) to determine the thickness of the layer of ganglion fibers.

METHOD

A total of 75 normal eyes were examined twice. Inter-individual variability was analyzed after standardizing the results. The coefficient of variation was used to measure the variability between tests, and the Pearson coefficient was used to analyze the correlation between variables.

RESULTS

The inter-individual variability was similar in GDx (8.9%) and OCT (11.1%), but very high in HRT (30.0%). No instrument detected significant changes with age. The coefficient of variation of the total thickness between the examinations of the same subject was significantly lower (P<.05) in GDx (1.4) than in OCT (2.0), but very high in HRT (6.4). The same was true when analyzing the upper fibers (GDx=1.8, OCT=2.9, HRT = 6.6), but not with the lower ones, where the only significant differences were observed with HRT (GDx = 2.2, OCT = 2.7, HRT = 7.0). Among the results of OCT and GDx, there was a significant correlation when comparing the first (r=0.46, P<.0001) and second examinations (r=0.52, P<.0001). However there was no significant relationship between the data provided by HRT for the two remaining instruments (P>.05).

CONCLUSIONS

There is a wide variation in the inter-individual and inter-test measurement of the thickness of the of nerve fibers layers using HRT. GDx has, in this respect, slight advantages over OCT.

Novel fractal feature-based multiclass glaucoma detection and progression prediction

We investigate the use of fractal analysis (FA) as the basis of a system for multiclass prediction of the progression of glaucoma. FA is applied to pseudo two-dimensional images converted from one-dimensional retinal nerve fiber layer (RNFL) data obtained from the eyes of normal subjects, and from subjects with progressive and non-progressive glaucoma. FA features are obtained using a box-counting method and a multi-fractional Brownian motion method that incorporates texture and multiresolution analyses. Both features are used for Gaussian kernel-based multiclass classification. Sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) are computed for the FA features and for metrics obtained using wavelet-Fourier analysis (WFA) and fast-Fourier analysis (FFA). The AUROCs that predict progressors from non-progressors based on classifiers trained using a dataset comprised of non-progressors and ocular normal subjects are 0.70, 0.71 and 0.82 for WFA, FFA, and FA, respectively. The correct multiclass classification rates among progressors, non-progressors, and ocular normal subjects are 0.82, 0.86 and 0.88 for WFA, FFA, and FA, respectively. Simultaneous multiclass classification among progressors, non-progressors, and ocular normal subjects has not been previously described. The novel FA-based features achieve better performance with fewer features and less computational complexity than WFA and FFA.