Dataset Information

Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

ABSTRACT:

Purpose

To compare the diagnostic performance of automated imaging for glaucoma.

Design

Prospective, direct comparison study.

Participants

Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.

Methods

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.

Main outcome measures

Sensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests.

Results

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.

Conclusions

Automated imaging technologies can aid clinicians in diagnosing glaucoma, but may not replace current strategies because they can miss some cases of severe glaucoma.

SUBMITTER: Banister K

PROVIDER: S-EPMC4846823 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

Banister Katie K Boachie Charles C Bourne Rupert R Cook Jonathan J Burr Jennifer M JM Ramsay Craig C Garway-Heath David D Gray Joanne J McMeekin Peter P Hernández Rodolfo R Azuara-Blanco Augusto A

Ophthalmology 20160323 5

<h4>Purpose</h4>To compare the diagnostic performance of automated imaging for glaucoma.<h4>Design</h4>Prospective, direct comparison study.<h4>Participants</h4>Adults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.<h4>Methods</h4>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) ...[more]

PMID: 27016459

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Project description:PurposeTo assess 3-dimensional surface shape patterns of the optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) in glaucoma with unsupervised artificial intelligence (AI).DesignRetrospective study.ParticipantsPatients with OCT scans obtained between 2016 and 2020 from Massachusetts Eye and Ear.MethodsThe first reliable Cirrus (Carl Zeiss Meditec, Inc) ONH OCT scans from each eye were selected. The ONH and RNFL surface shape was represented by the vertical positions of the inner limiting membrane (ILM) relative to the lowest ILM vertical position in each eye. Nonnegative matrix factorization was applied to determine the ONH and RNFL surface shape patterns, which then were correlated with OCT and visual field (VF) loss parameters and subsequent VF loss rate. We tested whether using ONH and RNFL surface shape patterns improved the prediction accuracy for associated VF loss and subsequent VF loss rates measured by adjusted r 2 and Bayesian information criterion (BIC) difference compared with using established OCT parameters alone.Main outcome measuresOptic nerve head and RNFL surface shape patterns and prediction of the associated VF loss and subsequent VF loss rates.ResultsWe determined 14 ONH and RNFL surface shape patterns using 9854 OCT scans from 5912 participants. Worse mean deviation (MD) was most correlated (r = 0.29 and r = 0.24, Pearson correlation; each P < 0.001) with lower coefficients of patterns 10 and 12 representing inferior and superior para-ONH nerve thinning, respectively. Worse MD was associated most with higher coefficients of patterns 5, 4, and 9 (r = -0.16, r = -0.13, and r = -0.13, respectively), representing higher peripheral ONH and RNFL surfaces. In addition to established ONH summary parameters and 12-clock-hour RNFL thickness, using ONH and RNFL surface patterns improved (BIC decrease: 182, 144, and 101, respectively; BIC decrease ≥ 6; strong model improvement) the prediction of accompanied MD (r 2 from 0.32 to 0.37), superior (r 2 from 0.27 to 0.31), and inferior (r 2 from 0.17 to 0.21) paracentral loss and improved (BIC decrease: 8 and 8, respectively) the prediction of subsequent VF MD loss rates (r 2 from 0 to 0.13) and inferior paracentral loss rates (r 2 from 0 to 0.16).ConclusionsThe ONH and RNFL surface shape patterns quantified by unsupervised AI techniques improved the structure-function relationship and subsequent VF loss rate prediction.

Project description:SIGNIFICANCE:This study resulted in the identification of an optic nerve head (ONH) feature associated with tilted optic discs, which might potentially contribute to ONH pathologies. Knowledge of such findings will enhance clinical insights and drive future opportunities to understand disease processes related to tilted optic discs. PURPOSE:The aim of this study was to identify novel retinal nerve fiber layer (RNFL) anomalies by evaluating tilted optic discs using optical coherence tomography. An observed retinal nerve fiber protrusion was further investigated for association with other morphological or functional parameters. METHODS:A retrospective review of 400 randomly selected adult patients with ONH examinations was conducted in a referral-only, diagnostic imaging center. After excluding other ONH pathologies, 215 patients were enrolled and evaluated for optic disc tilt and/or torsion. Gross anatomical ONH features, including size and rim or parapapillary region elevation, were assessed with stereoscopic fundus photography. Optical coherence tomography provided detailed morphological information of individual retinal layers. Statistical analysis was applied to identify significant changes between individual patient cohorts. RESULTS:A dome-shaped hyperreflective RNFL bulge, protruding into the neurosensory retina at the optic disc margins, was identified in 17 eyes with tilted optic discs. Available follow-up data were inconclusive regarding natural changes with this ONH feature. This RNFL herniation was significantly correlated with smaller than average optic disc size (P = .005), congenital disc tilt (P < .0001), and areas of rim or parapapillary elevation (P < .0001). CONCLUSIONS:This study reports an RNFL protrusion associated with tilted optic discs, which has not previously been assessed as an independent ONH structure. The feature is predominantly related to congenital crowded, small optic discs and variable between patients. This study is an important first step to elucidate diagnostic capabilities of tilted disc morphological changes and understanding associated functional deficits.

Dataset Information

Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

Purpose

Design

Participants

Methods

Main outcome measures

Results

Conclusions

Publications

Can Automated Imaging for Optic Disc and Retinal Nerve Fiber Layer Analysis Aid Glaucoma Detection?

Similar Datasets

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