Dataset Information

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.

ABSTRACT: BACKGROUND:Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc assessment are therefore needed to prevent sight loss. METHODS:We developed a novel glaucoma detection algorithm that segments and analyses colour photographs to quantify optic nerve rim consistency around the whole disc at 15-degree intervals. This provides a profile of the cup/disc ratio, in contrast to the vertical cup/disc ratio in common use. We introduce a spatial probabilistic model, to account for the optic nerve shape, we then use this model to derive a disc deformation index and a decision rule for glaucoma. We tested our algorithm on two separate image datasets (ORIGA and RIM-ONE). RESULTS:The spatial algorithm accurately distinguished glaucomatous and healthy discs on internal and external validation (AUROC 99.6% and 91.0% respectively). It achieves this using a dataset 100-times smaller than that required for deep learning algorithms, is flexible to the type of cup and disc segmentation (automated or semi-automated), utilises images with missing data, and is correlated with the disc size (p = 0.02) and the rim-to-disc at the narrowest rim (p<0.001, in external validation). DISCUSSION:The spatial probabilistic algorithm is highly accurate, highly data efficient and it extends to any imaging hardware in which the boundaries of cup and disc can be segmented, thus making the algorithm particularly applicable to research into disease mechanisms, and also glaucoma screening in low resource settings.

SUBMITTER: MacCormick IJC

PROVIDER: S-EPMC6328156 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.

MacCormick Ian J C IJC Williams Bryan M BM Zheng Yalin Y Li Kun K Al-Bander Baidaa B Czanner Silvester S Cheeseman Rob R Willoughby Colin E CE Brown Emery N EN Spaeth George L GL Czanner Gabriela G

PloS one 20190110 1

<h4>Background</h4>Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc asse ...[more]

PMID: 30629635

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Project description:Our objective was to compare the diagnostic accuracies of and to determine the correlations between the disc damage likelihood scale (DDLS) and anatomical and functional tests used for glaucoma detection. A total of 54 healthy subjects (54 eyes) and 47 primary open-angle glaucoma patients (47 eyes) were included in this cross-sectional observational study. DDLS scores and cup-to-disc (C/D) ratios were evaluated. Subjects underwent standard automated perimetry (SAP), optic disc and retinal nerve fiber layer (RNFL) imaging with time and spectral-domain optical coherence tomography (TD and SD-OCT), Heidelberg Retina Tomograph (HRT II), and scanning laser polarimetry (GDx-VCC). Areas under the receiver operating characteristic curves (AROCs) for DDLS and diagnostic tests parameters were calculated. DDLS correlations (Spearman's rank) among these parameters were analyzed. Fifty-four eyes were healthy and 47 had glaucoma, including 16 preperimetric glaucoma. DDLS, vertical and horizontal C/D ratios had the largest AROCs (0.92, 0.94 and 0.91, respectively). DDLS diagnostic accuracy was better than the accuracies of HRT II parameters, TD and SD-OCT RNFL thicknesses, and SAP mean deviation (MD) index. There were no significant differences between the accuracies of the DDLS and the C/D ratios, TD-OCT vertical (0.89) and horizontal (0.86) C/D ratios, TD-OCT C/D area ratio (0.89), and GDx-VCC NFI (0.81). DDLS showed significant strong correlations with vertical (r = 0.79) and horizontal (0.74) C/D ratios, and with the parameters vertical C/D ratio and C/D area ratio from HRT II (both 0.77) and TD-OCT (0.75 and 0.72, respectively). DDLS had significant moderate correlations with most of the other structural measurements and SAP MD. The optic disc clinical evaluation with DDLS system and C/D ratio demonstrated excellent accuracy in distinguishing glaucomatous from healthy eyes. DDLS had moderate to strong correlations with most structural and functional parameters. These findings stress the importance of optic disc clinical examination to detect glaucoma in a clinical scenario.

Project description:PurposeTo compare the diagnostic performance of automated imaging for glaucoma.DesignProspective, direct comparison study.ParticipantsAdults with suspected glaucoma or ocular hypertension referred to hospital eye services in the United Kingdom.MethodsWe 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 measuresSensitivity and specificity, likelihood ratios, diagnostic, odds ratio, and proportion of indeterminate tests.ResultsWe 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.ConclusionsAutomated imaging technologies can aid clinicians in diagnosing glaucoma, but may not replace current strategies because they can miss some cases of severe glaucoma.

Dataset Information

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.

Publications

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.

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