Dataset Information

Morphometric analysis and classification of glaucomatous optic neuropathy using radial polynomials.

ABSTRACT: To quantify the morphological features of the optic nerve head using radial polynomials, to use these morphometric models as the basis for classification of glaucomatous optic neuropathy via an automated decision tree induction algorithm, and to compare these classification results with established procedures.A cohort of patients with high-risk ocular hypertension or early glaucoma (n=179) and a second cohort of normal subjects (n=96) were evaluated for glaucomatous optic neuropathy using stereographic disc photography and confocal scanning laser tomography. Morphological features of the optic nerve head region were modeled from the tomography data using pseudo-Zernike radial polynomials and features derived from these models were used as the basis for classification by a decision tree induction algorithm. Decision tree classification performance was compared with expert classification of stereographic disc photographs and analysis of neural retinal rim thickness by Moorfields Regression Analysis (MRA).Root mean squared error of the morphometric models decreased asymptotically with additional polynomial coefficients, from 62±0.5 (32 coefficients) to 32±5.7 ?m (256 coefficients). Optimal morphometric classification was derived from a subset of 64 total features and had low sensitivity (69%), high specificity (88%), very good accuracy (80%), and area under the receiver operating characteristic curve (AUROC) was 88% (95% confidence interval, 78%-98%). In comparison, MRA classification of the same records had a comparatively poorer sensitivity (55%), but had higher specificity (95%), with similar overall accuracy (78%) and AUROC curve, 83% (95% CI, 70%-96%).Pseudo-Zernike radial polynomials provide a mathematically compact and faithful morphological representation of the structural features of the optic nerve head. This morphometric method of glaucomatous optic neuropathy classification has greater sensitivity, and similar overall classification performance (AUROC) when compared with classification by neural retinal rim thickness by MRA in patients with high-risk ocular hypertension and early glaucoma.

SUBMITTER: Twa MD

PROVIDER: S-EPMC3163012 | biostudies-literature | 2012 Jun-Jul

REPOSITORIES: biostudies-literature

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Morphometric analysis and classification of glaucomatous optic neuropathy using radial polynomials.

Twa Michael D MD Parthasarathy Srinivasan S Johnson Chris A CA Bullimore Mark A MA

Journal of glaucoma 20120601 5

<h4>Purpose</h4>To quantify the morphological features of the optic nerve head using radial polynomials, to use these morphometric models as the basis for classification of glaucomatous optic neuropathy via an automated decision tree induction algorithm, and to compare these classification results with established procedures.<h4>Methods</h4>A cohort of patients with high-risk ocular hypertension or early glaucoma (n=179) and a second cohort of normal subjects (n=96) were evaluated for glaucomato ...[more]

PMID: 21423035

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Project description:ImportanceA deep learning system (DLS) that could automatically detect glaucomatous optic neuropathy (GON) with high sensitivity and specificity could expedite screening for GON.ObjectiveTo establish a DLS for detection of GON using retinal fundus images and glaucoma diagnosis with convoluted neural networks (GD-CNN) that has the ability to be generalized across populations.Design, setting, and participantsIn this cross-sectional study, a DLS for the classification of GON was developed for automated classification of GON using retinal fundus images obtained from the Chinese Glaucoma Study Alliance, the Handan Eye Study, and online databases. The researchers selected 241 032 images were selected as the training data set. The images were entered into the databases on June 9, 2009, obtained on July 11, 2018, and analyses were performed on December 15, 2018. The generalization of the DLS was tested in several validation data sets, which allowed assessment of the DLS in a clinical setting without exclusions, testing against variable image quality based on fundus photographs obtained from websites, evaluation in a population-based study that reflects a natural distribution of patients with glaucoma within the cohort and an additive data set that has a diverse ethnic distribution. An online learning system was established to transfer the trained and validated DLS to generalize the results with fundus images from new sources. To better understand the DLS decision-making process, a prediction visualization test was performed that identified regions of the fundus images utilized by the DLS for diagnosis.ExposuresUse of a deep learning system.Main outcomes and measuresArea under the receiver operating characteristics curve (AUC), sensitivity and specificity for DLS with reference to professional graders.ResultsFrom a total of 274 413 fundus images initially obtained from CGSA, 269 601 images passed initial image quality review and were graded for GON. A total of 241 032 images (definite GON 29 865 [12.4%], probable GON 11 046 [4.6%], unlikely GON 200 121 [83%]) from 68 013 patients were selected using random sampling to train the GD-CNN model. Validation and evaluation of the GD-CNN model was assessed using the remaining 28 569 images from CGSA. The AUC of the GD-CNN model in primary local validation data sets was 0.996 (95% CI, 0.995-0.998), with sensitivity of 96.2% and specificity of 97.7%. The most common reason for both false-negative and false-positive grading by GD-CNN (51 of 119 [46.3%] and 191 of 588 [32.3%]) and manual grading (50 of 113 [44.2%] and 183 of 538 [34.0%]) was pathologic or high myopia.Conclusions and relevanceApplication of GD-CNN to fundus images from different settings and varying image quality demonstrated a high sensitivity, specificity, and generalizability for detecting GON. These findings suggest that automated DLS could enhance current screening programs in a cost-effective and time-efficient manner.

Project description:PurposeTo determine whether an association exists between sleep apnea and open-angle glaucoma, normal-tension glaucoma, nonarteritic ischemic optic neuropathy (NAION), papilledema, or idiopathic intracranial hypertension (IIH) and whether treatment with continuous positive airway pressure affects the development of these conditions.DesignRetrospective, longitudinal cohort study.MethodsBilling records for beneficiaries 40 years of age and older enrolled in a large United States managed care network from 2001 through 2007 were reviewed. Incidence of open-angle glaucoma, normal-tension glaucoma, NAION, papilledema, and IIH were determined for the beneficiaries and were stratified by sleep apnea status. Cox regression analyses determined the hazard of each of these conditions developing among individuals with and without sleep apnea, with adjustment for sociodemographic, ocular, and medical conditions.ResultsAmong the 2 259 061 individuals in the study, 156 336 (6.9%) had 1 or more sleep apnea diagnoses. The hazard of open-angle glaucoma was no different among persons with sleep apnea either treated (adjusted hazard ratio [HR], 0.99; 95% confidence interval [CI], 0.82 to 1.18) or untreated with continuous positive airway pressure (HR, 1.01; 95% CI, 0.98 to 1.05) and individuals without sleep apnea. Similar findings were observed when assessing the hazard of normal-tension glaucoma developing (P > .05 for both comparisons). A significantly increased hazard of NAION developing (HR, 1.16; 95% CI, 1.01 to 1.33) and IIH (HR, 2.03; 95% CI, 1.65 to 2.49) was observed among individuals with sleep apnea who were not receiving continuous positive airway pressure therapy as compared with individuals without sleep apnea, although similar increased risks could not be demonstrated among continuous positive airway pressure-treated sleep apnea patients for these conditions (P > .05 for both comparisons).ConclusionsPatients with untreated sleep apnea are at increased risk for IIH and NAION. Clinicians should consider appropriate screening for these conditions in sleep apnea patients.

Dataset Information

Morphometric analysis and classification of glaucomatous optic neuropathy using radial polynomials.

Publications

Morphometric analysis and classification of glaucomatous optic neuropathy using radial polynomials.

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