Comparison Between Automated And Microscopic Analysis In Body Fluids Cytology.

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Vertical Bone Measurements From Cone Beam Computed Tomography Images Using Different Software Packages.

This article aimed at comparing the accuracy of linear measurement tools of different commercial software packages. Eight fully edentulous dry mandibles were selected for this study. Incisor, canine, premolar, first molar and second molar regions were selected. Cone beam computed tomography (CBCT) images were obtained with i-CAT Next Generation. Linear bone measurements were performed by one observer on the cross-sectional images using three different software packages: XoranCat®, OnDemand3D® and KDIS3D®, all able to assess DICOM images. In addition, 25% of the sample was reevaluated for the purpose of reproducibility. The mandibles were sectioned to obtain the gold standard for each region. Intraclass coefficients (ICC) were calculated to examine the agreement between the two periods of evaluation; the one-way analysis of variance performed with the post-hoc Dunnett test was used to compare each of the software-derived measurements with the gold standard. The ICC values were excellent for all software packages. The least difference between the software-derived measurements and the gold standard was obtained with the OnDemand3D and KDIS3D (-0.11 and -0.14 mm, respectively), and the greatest, with the XoranCAT (+0.25 mm). However, there was no statistical significant difference between the measurements obtained with the different software packages and the gold standard (p> 0.05). In conclusion, linear bone measurements were not influenced by the software package used to reconstruct the image from CBCT DICOM data.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.