Paired Evaluation Of Calvarial Reconstruction With Prototyped Titanium Implants With And Without Ceramic Coating.

To investigate the osseointegration properties of prototyped implants with tridimensionally interconnected pores made of the Ti6Al4V alloy and the influence of a thin calcium phosphate coating. Bilateral critical size calvarial defects were created in thirty Wistar rats and filled with coated and uncoated implants in a randomized fashion. The animals were kept for 15, 45 and 90 days. Implant mechanical integration was evaluated with a push-out test. Bone-implant interface was analyzed using scanning electron microscopy. The maximum force to produce initial displacement of the implants increased during the study period, reaching values around 100N for both types of implants. Intimate contact between bone and implant was present, with progressive bone growth into the pores. No significant differences were seen between coated and uncoated implants. Adequate osseointegration can be achieved in calvarial reconstructions using prototyped Ti6Al4V Implants with the described characteristics of surface and porosity.

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.