Biomedical subjects

Zvia Burgansky-Eliash

Publications and source records attributed to Zvia Burgansky-Eliash.

3 recordsLinked to original sources

PubMed2007

Glaucoma detection with matrix and standard achromatic perimetry.

BACKGROUND: Matrix perimetry is a new iteration of frequency-doubling technology (FDT) which uses a smaller target size in the standard achromatic perimetry presentation pattern. AIM: To compare the performance of matrix and Swedish interactive thresholding algorithm (SITA) perimetry in detecting glaucoma diagnosed by structural assessment. DESIGN: Prospective cross-sectional study. METHODS: 76 eyes from 15 healthy subjects and 61 consecutive glaucoma suspects and patients with glaucoma were included. All patients underwent optic nerve head (ONH) photography, SITA and matrix perimetries, and optical coherence tomography (OCT) within a 6-month period. Glaucoma diagnosis was established by either glaucomatous optic neuropathy or OCT by assessing retinal nerve fibre layer (RNFL) thickness. Mean deviation (MD), pattern standard deviation (PSD), glaucoma hemifield test and cluster of abnormal testing locations were recorded from matrix and SITA perimetries. RESULTS: Similar correlations were observed with matrix and SITA perimetry MD and PSD with either cup-to-disc ratio or OCT mean RNFL. The area under the receiver operating characteristic (AROC) curves of MD and PSD for discriminating between healthy and glaucomatous eyes ranged from 0.69 to 0.81 for matrix perimetry and from 0.75 to 0.77 for SITA perimetry. There were no significant differences among any corresponding matrix and SITA perimetry AROCs. CONCLUSIONS: Matrix and SITA perimetries had similar capabilities for distinguishing between healthy and glaucomatous eyes regardless of whether the diagnosis was established by ONH or OCT-RNFL assessment.

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PubMed2006

Glaucoma detection with the Heidelberg retina tomograph 3.

PURPOSE: To compare the ability of the Heidelberg retina tomograph version 3 (HRT 3) and HRT version 2 (HRT 2) to discriminate between healthy and glaucomatous eyes. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: Seventy-one eyes of 71 healthy volunteers and 50 eyes of 50 glaucoma patients were studied. The average visual field mean deviation of the glaucoma group was -6.03+/-5.78 dB. INTERVENTION: All participants had comprehensive ocular examinations, perimetry, and HRT scanning within 6 months. HRT 2 data were analyzed using HRT 3 software without modifying the disc margin. MAIN OUTCOME MEASURES: Discrimination capabilities between healthy and glaucomatous eyes were determined by areas under the receiver operating characteristics (AROCs) curves. Comparisons between corresponding AROCs obtained by HRT 2 and HRT 3 analyses were performed using the nonparametric DeLong method. Agreement between classifications as defined by the different analysis methods was quantified by kappa analysis. RESULTS: The individual stereometric parameters with the best discrimination were linear cup/disc ratio (AROC = 0.897; 95% confidence interval [CI], 0.836-0.958) for standard HRT 3 analysis and horizontal retinal nerve fiber layer curvature (0.905) for HRT 3 glaucoma probability score (GPS) analysis. Areas under the receiver operating characteristics for discrimination between glaucomatous and healthy eyes of the overall classification by HRT 2 Moorfields regression analysis (MRA), HRT 3 MRA, and GPS were 0.927 (95% CI, 0.877-0.977), 0.934 (0.888-0.980), and 0.880 (0.812-0.948), respectively. The difference between the 3 AROCs was not significant (P = 0.44). The agreement between HRT 2 and HRT 3 overall MRA classification was good (kappa = 0.70; CI, 0.59-0.80) with HRT 3 tending to report more abnormalities than HRT 2 analysis. The agreement between overall HRT 3 MRA and overall GPS was kappa = 0.58 (CI, 0.45-0.70). CONCLUSIONS: The glaucoma discriminating ability of the new HRT 3 software is similar to that of the previous generation HRT 2. The GPS analysis showed promising results in differentiating between healthy and glaucomatous eyes without the need for subjective operator input.

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PubMed2005

Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study.

PURPOSE: Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. METHODS: Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] > or = -6 dB) and 20 had advanced glaucoma (MD < -6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. RESULTS: The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). CONCLUSIONS: Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality.

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