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Ophthalmology. 2017 Aug;124(8):1218-1228. doi: 10.1016/j.ophtha.2017.03.044. Epub 2017 Apr 29.

Diagnostic Accuracy of Spectralis SD OCT Automated Macular Layers Segmentation to Discriminate Normal from Early Glaucomatous Eyes.

Author information

1
Department of Ophthalmology, Hospital de l'Esperança-Parc de Salut Mar, Universitat Pompeu Fabra, Barcelona, Spain; Institut Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Institut de la Màcula, Centre Mèdic Teknon, Barcelona, Spain. Electronic address: martapazoslopez@gmail.com.
2
Department of Ophthalmology, Hospital de l'Esperança-Parc de Salut Mar, Universitat Pompeu Fabra, Barcelona, Spain; Department of Retina, Institut Català de la Retina (ICR), Barcelona, Spain.
3
Institut de la Màcula, Centre Mèdic Teknon, Barcelona, Spain.
4
Department of Glaucoma, Institut Català de la Retina (ICR), Barcelona, Spain.
5
Department of Retina, Institut Català de la Retina (ICR), Barcelona, Spain; Department of Glaucoma, Institut Català de la Retina (ICR), Barcelona, Spain.
6
Department of Ophthalmology, Hospital de l'Esperança-Parc de Salut Mar, Universitat Pompeu Fabra, Barcelona, Spain.
7
Department of Ophthalmology, Hospital de l'Esperança-Parc de Salut Mar, Universitat Pompeu Fabra, Barcelona, Spain; Institut Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain; Department of Glaucoma, Institut Català de la Retina (ICR), Barcelona, Spain; Department of Ophthalmology, Universitat Internacional de Catalunya (UIC), Barcelona, Spain.

Abstract

PURPOSE:

To evaluate the accuracy of the macular retinal layer segmentation software of the Spectralis spectral-domain (SD) optical coherence tomography (OCT) device (Heidelberg Engineering, Inc., Heidelberg, Germany) to discriminate between healthy and early glaucoma (EG) eyes.

DESIGN:

Prospective, cross-sectional study.

PARTICIPANTS:

Forty EG eyes and 40 healthy controls were included.

METHODS:

All participants were examined using the standard posterior pole and the peripapillary retinal nerve fiber layer (pRNFL) protocols of the Spectralis OCT device. Using an Early Treatment Diagnostic Retinopathy Study circle at the macular level, the automated retinal segmentation software was applied to determine thicknesses of the following parameters: total retinal thickness, inner retinal layer (IRL), macular retinal nerve fiber layer (mRNFL), macular ganglion cell layer (mGCL), macular inner plexiform layer (mIPL), macular inner nuclear layer (mINL), macular outer plexiform layer (mOPL), macular outer nuclear layer (mONL), photoreceptors (PR), and retinal pigmentary epithelium (RPE). The ganglion cell complex (GCC) was determined by adding the mRNFL, mGCL, and mIPL parameters and the ganglion cell layer-inner plexiform layer (mGCL-IPL) was determined by combining the mGCL and mIPL parameters. Thickness of each layer was compared between the groups, and the layer and sector with the best area under the receiver operating characteristic curve (AUC) were identified.

MAIN OUTCOME MEASURES:

Comparison of pRNFL, IRL, mRNFL, mGCL, mIPL, mGCC, mGCL-IPL, mINL, mOPL, mONL, PR, and RPE parameters and total retinal thicknesses between groups for the different areas and their corresponding AUCs.

RESULTS:

Peripapillary RNFL was significantly thinner in the EG group globally and in all 6 sectors assessed (P < 0.0005). For the macular variables, retinal thickness was significantly reduced in the EG group for total retinal thickness, mIRL, mRNFL, mGCL, and mIPL. The 2 best isolated parameters to discriminate between the 2 groups were pRNFL (AUC, 0.956) and mRNFL (AUC, 0.906). When mRNFL, mGCL, and mIPL measurements were combined (mGCC and mGCL plus mIPL), then its diagnostic performance improved (AUC, 0.940 and 0.952, respectively).

CONCLUSIONS:

Macular RNFL, mGCL-IPL, and mGCC measurements showed a high diagnostic capability to discriminate between healthy and EG participants. However, macular intraretinal measurements still have not overcome standard pRNFL parameters.

PMID:
28461015
DOI:
10.1016/j.ophtha.2017.03.044
[Indexed for MEDLINE]

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