Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

Integration and fusion of standard automated perimetry and optical coherence tomography data for improved automated glaucoma diagnostics

<p>Abstract</p> <p>Background</p> <p>The performance of glaucoma diagnostic systems could be conceivably improved by the integration of functional and structural test measurements that provide relevant and complementary information for reaching a diagnosis. The purpose of this study was to investigate the performance of data fusion methods and techniques for simple combination of Standard Automated Perimetry (SAP) and Optical Coherence Tomography (OCT) data for the diagnosis of glaucoma using Artificial Neural Networks (ANNs).</p> <p>Methods</p> <p>Humphrey 24-2 SITA standard SAP and StratusOCT tests were prospectively collected from a randomly selected population of 125 healthy persons and 135 patients with glaucomatous optic nerve heads and used as input for the ANNs. We tested commercially available standard parameters as well as novel ones (fused OCT and SAP data) that exploit the spatial relationship between visual field areas and sectors of the OCT peripapillary scan circle. We evaluated the performance of these SAP and OCT derived parameters both separately and in combination.</p> <p>Results</p> <p>The diagnostic accuracy from a combination of fused SAP and OCT data (95.39%) was higher than that of the best conventional parameters of either instrument, i.e. SAP Glaucoma Hemifield Test (p < 0.001) and OCT Retinal Nerve Fiber Layer Thickness ≥ 1 quadrant (p = 0.031). Fused OCT and combined fused OCT and SAP data provided similar Area under the Receiver Operating Characteristic Curve (AROC) values of 0.978 that were significantly larger (p = 0.047) compared to ANNs using SAP parameters alone (AROC = 0.945). On the other hand, ANNs based on the OCT parameters (AROC = 0.970) did not perform significantly worse than the ANNs based on the fused or combined forms of input data. The use of fused input increased the number of tests that were correctly classified by both SAP and OCT based ANNs.</p> <p>Conclusions</p> <p>Compared to the use of SAP parameters, input from the combination of fused OCT and SAP parameters, and from fused OCT data, significantly increased the performance of ANNs. Integrating parameters by including a priori relevant information through data fusion may improve ANN classification accuracy compared to currently available methods.</p

Rapid and sensitive methodology for determination of ethyl carbamate in fortified wines using microextraction by packed sorbent and gas chromatography with mass spectrometric detection

This work presents a new methodology to quantify ethyl carbamate (EC) in fortified wines. The presented approach combines the microextraction by packed sorbent (MEPS), using a hand-held automated analytical syringe, with one-dimensional gas chromatography coupled with mass spectrometry detection (GC–MS). The performance of different MEPS sorbent materials was tested, namely SIL, C2, C8, C18, and M1. Also, several extraction solvents and the matrix effect were evaluated. Experimental data showed that C8 and dichloromethane were the best sorbent/solvent pair to extract EC. Concerning solvent and sample volumes optimization used in MEPS extraction an experimental design (DoE) was carried out. The best extraction yield was achieved passing 300 μL of sample and 100 μL of dichloromethane. The method validation was performed using a matrix-matched calibration using both sweet and dry fortified wines, to minimize the matrix effect. The proposed methodology presented good linearity (R2 = 0.9999) and high sensitivity, with quite low limits of detection (LOD) and quantification (LOQ), 1.5 μg L−1 and 4.5 μg L−1, respectively. The recoveries varied between 97% and 106%, while the method precision (repeatability and reproducibility) was lower than 7%. The applicability of the methodology was confirmed through the analysis of 16 fortified wines, with values ranging between 7.3 and 206 μg L−1. All chromatograms showed good peak resolution, confirming its selectivity. The developed MEPS/GC–MS methodology arises as an important tool to quantify EC in fortified wines, combining efficiency and effectiveness, with simpler, faster and affordable analytical procedures that provide great sensitivity without using sophisticated and expensive equipment

Procjena unosa bakra umjerenom konzumacijom vina

To estimate Cu exposure level from wine consumption and to assess possible health risk for moderate wine consumers, wine samples were collected from different wine-growing areas of Croatia. Median concentrations were 180 μg L-1, range (76 to 292) μg L-1, in commercial wines and 258 μg L-1, range (115 to 7600) μg L-1, in homemade wines (P>0.05). Maximum permitted level of 1000 μg L-1 was exceeded in three homemade wines. However, daily intake of Cu from wine (in the range from 0.02 mg d-1 to 1.52 mg d-1) estimated from Cu concentration in all wine samples is lower than the tolerable upper intake level of 5 mg d-1 proposed by the EU Scientifi c Committee on Food and does not present a risk to moderate wine consumers.Kako bi se procijenila razina izloženosti bakru prilikom konzumacije vina te utvrdili mogući zdravstveni rizici za umjerene potrošače vina, skupljeni su i ispitani uzorci vina iz različitih vinogradarskih područja Hrvatske. Koncentracije Cu bile su u rasponu od 76 μg L-1 do 292 μg L-1 (medijan 180 μg L-1) u komercijalnim vinima te od 115 μg L-1 do 7.600 μg L-1 (medijan 258 μg L-1) u vinima domaće proizvodnje (P>0,05). U tri ispitana vina domaće proizvodnje koncentracija Cu bila je iznad najviše dopuštene od 1000 μg L-1. Međutim, izračunani dnevni unos Cu u slučaju konzumacije ispitanih vina (u rasponu od 0,02 mg d-1 do 1,52 mg d-1) ne prelazi gornju granicu tolerancije unosa od 5 mg d-1 te nije zdravstveni rizik umjerenim potrošačima vina