Dataset Information

Optic Disc, Macula, and Retinal Nerve Fiber Layer Measurements Obtained by OCT in Thyroid-Associated Ophthalmopathy.

ABSTRACT: Aim. To compare the measurements of retinal nerve fiber layer (RNFL), macula and optic disc parameters obtained by optical coherence tomography (OCT), and intraocular pressure (IOP) between the patients with thyroid-associated ophthalmopathy (TAO) and healthy controls. Methods. One hundred and thirty-two eyes of 66 patients with TAO and 72 eyes of 36 healthy controls were included in the study. Proptosis level was determined by Hertel exophthalmometer. Optic disc, peripapillary retinal nerve fiber layer, and macula parameters were measured by OCT. All measurements of the patients were compared with those of age- and sex-matched healthy controls. Results. No statistically significant difference was found between the patients with TAO and control group in terms of demographic characteristics (P > 0.05). Exophthalmometer measurements and IOP were higher in TAO group (P < 0.05). Mean macula thicknesses in TAO and control groups were 239.3 ± 29.8??m and 246.6 ± 31.8??m, respectively, and the difference between the groups was statistically significant (P = 0.000). TAO group had thinner inferior RNFL thickness and macular thicknesses (superior, inferior, temporal, and nasal) and higher disc area and C/D ratio when compared with the control group (P < 0.05). Conclusion. IOP, disc area, and C/D area ratio were higher in the patients with TAO and the thicknesses of macula and inferior RNFL were thinner when compared with healthy controls. This trial is registered with registration number at clinicaltrials.gov NCT02766660.

SUBMITTER: Say?n O

PROVIDER: S-EPMC4967470 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Optic Disc, Macula, and Retinal Nerve Fiber Layer Measurements Obtained by OCT in Thyroid-Associated Ophthalmopathy.

Sayın Osman O Yeter Volkan V Arıtürk Nurşen N

Journal of ophthalmology 20160717

Aim. To compare the measurements of retinal nerve fiber layer (RNFL), macula and optic disc parameters obtained by optical coherence tomography (OCT), and intraocular pressure (IOP) between the patients with thyroid-associated ophthalmopathy (TAO) and healthy controls. Methods. One hundred and thirty-two eyes of 66 patients with TAO and 72 eyes of 36 healthy controls were included in the study. Proptosis level was determined by Hertel exophthalmometer. Optic disc, peripapillary retinal nerve fib ...[more]

PMID: 27493796

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Project description:PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

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Project description:ObjectiveTo explore participant-level biological attributes and scan-level methodological attributes associated with retinal nerve fiber layer (RNFL) thickness variability in a population-based sample of elderly United States adults.DesignCross-sectional analysis using data from the Framingham Heart Study.ParticipantsOne thousand three hundred forty-seven eyes from 825 participants with ≥1 OCT scan and axial length data were included.MethodsThree or more successive RNFL scans of each eye of each participant were obtained in a single session. Multivariable linear mixed models were employed to explore the associations between average RNFL thickness with participant-level biological attributes (age, gender, race, ethnicity, and axial length) and scan-level attributes (signal strength [SS]) as independent variables in the whole population as well as a subsample of adults with no self-reported history of glaucoma. Similar analyses were designed to assess methodological variability with average within-eye standard deviation (SD) for repeated scans as the dependent variable.Main outcomes measures(1) Biological variability: average RNFL thickness, and (2) methodological variability: average within-participant SD across repeated scans.ResultsAge (β = - 0.19 microns/year, [95% confidence interval {CI}: - 0.29, - 0.09]), female gender (β = +1.48 microns vs. male, [95% CI: 0.09, 2.86]), axial length (β = - 1.24 microns/mm of greater length, [95% CI: - 1.80, - 0.67]), and SS (β = +1.62 microns/1 unit greater SS, [95% CI: 1.16, 2.09]) were significantly associated with RNFL thickness, while race and ethnicity were not (P > 0.05). In analyses designed to assess methodological variability, higher RNFL thickness (β = +0.02 per micron increase, [95% CI: 0.01, 0.03]), and lower SS (β = +0.19 per 1 unit lower SS, [95% CI: 0.10, 0.27]) were significantly associated with greater RNFL variability. In adults with no self-reported history of glaucoma (n of eyes = 1165, n of participants = 712), female gender was not associated with RNFL, while African American race was associated with thicker RNFL (β = +4.65 microns vs. Whites, [95% CI: 1.28, 8.03]).ConclusionsRetinal nerve fiber layer thickness is lower with older age, male gender, greater axial length, lower SS, and Whites (as compared with African Americans) without self-reported glaucoma. Measurement variability (SD) is higher with greater RNFL thickness and lower SS. Understanding these biological and methodological variations is important to aid in OCT interpretation.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

Optic Disc, Macula, and Retinal Nerve Fiber Layer Measurements Obtained by OCT in Thyroid-Associated Ophthalmopathy.

Publications

Optic Disc, Macula, and Retinal Nerve Fiber Layer Measurements Obtained by OCT in Thyroid-Associated Ophthalmopathy.

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