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1.  Scan quality effect on glaucoma discrimination by glaucoma imaging devices 
The British journal of ophthalmology  2009;93(12):1580-1584.
To evaluate, within ocular imaging scans of acceptable quality as determined by manufacturers' guidelines, the effects of image quality on glaucoma discrimination capabilities.
One hundred and four healthy and 75 glaucomatous eyes from the Advanced Imaging in Glaucoma Study (AIGS) were imaged with GDx-VCC, HRT II and StratusOCT. Quality score (QS≥8), pixel standard deviation (SD≤50) and signal strength (SS≥5) were used as quality parameter cut-offs, respectively. GDx nerve fibre indicator (NFI) and HRT Moorfields regression analysis (MRA) classifications and OCT mean retinal nerve fibre layer (RNFL) thickness were used as the discriminatory parameters. Logistic regression models were used to model the dichotomous clinical classification (healthy vs glaucoma) as a function of image-quality parameters and discriminatory parameters.
Quality parameter covariates were statistically non-significant for GDx and HRT but had an inverse effect on OCT in predicting disease (a higher SS had a lower probability of glaucoma). Age was a significant covariate for GDx and HRT, but not OCT, while ethnicity and interaction between the image quality and the institute where scans were acquired were significant covariates in the OCT models.
Scan quality within the range recommended as acceptable by the manufacturer of each imaging device does not affect the glaucoma discriminating ability of GDx or HRT but does affect Stratus OCT glaucoma discrimination.
PMCID: PMC2917916  PMID: 19692363
2.  Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection 
To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes.
Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies.
The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/ D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875.
Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma.
PMCID: PMC2916743  PMID: 18523087
3.  Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography 
The British journal of ophthalmology  2009;93(8):1057-1063.
To investigate retinal nerve fibre layer (RNFL) thickness measurement reproducibility using conventional time-domain optical coherence tomography (TD-OCT) and spectral-domain OCT (SD-OCT), and to evaluate two methods defining the optic nerve head (ONH) centring: Centred Each Time (CET) vs Centred Once (CO), in terms of RNFL thickness measurement variability on SD-OCT.
Twenty-seven eyes (14 healthy subjects) had three circumpapillary scans with TD-OCT and three raster scans (three-dimensional or 3D image data) around ONH with SD-OCT. SD-OCT images were analysed in two ways: (1) CET: ONH centre was defined on each image separately and (2) CO: ONH centre was defined on one image and exported to other images after scan registration. After defining the ONH centre, a 3.4 mm diameter virtual circular OCT was resampled on SD-OCT images to mimic the conventional circumpapillary RNFL thickness measurements taken with TD-OCT.
CET and CO showed statistically significantly better reproducibility than TD-OCT except for 11:00 with CET. CET and CO methods showed similar reproducibility.
SD-OCT 3D cube data generally showed better RNFL measurement reproducibility than TD-OCT. The choice of ONH centring methods did not affect RNFL measurement reproducibility.
PMCID: PMC2861342  PMID: 19429591

Results 1-3 (3)