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:To compare the diagnostic performance of automated imaging for glaucoma.Prospective, direct comparison study.Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.We evaluated 4 automated imaging test algorithms: the Heidelberg Retinal Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany) glaucoma probability score (GPS), the HRT Moorfields regression analysis (MRA), scanning laser polarimetry (GDx enhanced corneal compensation; Glaucoma Diagnostics (GDx), Carl Zeiss Meditec, Dublin, CA) nerve fiber indicator (NFI), and Spectralis optical coherence tomography (OCT; Heidelberg Engineering) retinal nerve fiber layer (RNFL) classification. We defined abnormal tests as an automated classification of outside normal limits for HRT and OCT or NFI ? 56 (GDx). We conducted a sensitivity analysis, using borderline abnormal image classifications. The reference standard was clinical diagnosis by a masked glaucoma expert including standardized clinical assessment and automated perimetry. We analyzed 1 eye per patient (the one with more advanced disease). We also evaluated the performance according to severity and using a combination of 2 technologies.Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests.We recruited 955 participants, and 943 were included in the analysis. The average age was 60.5 years (standard deviation, 13.8 years); 51.1% were women. Glaucoma was diagnosed in at least 1 eye in 16.8%; 32% of participants had no glaucoma-related findings. The HRT MRA had the highest sensitivity (87.0%; 95% confidence interval [CI], 80.2%-92.1%), but lowest specificity (63.9%; 95% CI, 60.2%-67.4%); GDx had the lowest sensitivity (35.1%; 95% CI, 27.0%-43.8%), but the highest specificity (97.2%; 95% CI, 95.6%-98.3%). The HRT GPS sensitivity was 81.5% (95% CI, 73.9%-87.6%), and specificity was 67.7% (95% CI, 64.2%-71.2%); OCT sensitivity was 76.9% (95% CI, 69.2%-83.4%), and specificity was 78.5% (95% CI, 75.4%-81.4%). Including only eyes with severe glaucoma, sensitivity increased: HRT MRA, HRT GPS, and OCT would miss 5% of eyes, and GDx would miss 21% of eyes. A combination of 2 different tests did not improve the accuracy substantially.Automated imaging technologies can aid clinicians in diagnosing glaucoma, but may not replace current strategies because they can miss some cases of severe glaucoma.

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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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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