The authors' aim was to provide volumetric data of mandibular condyles based upon cone beam computed tomography with the means of semiautomatic segmentation.Retrospective chart review of 350 patients (700 mandibular condyles) with cone beam computerized tomography between February 2007 and December 2016. Univariate analysis was performed to analyze associations between variables. P values <0.05 were considered significant. Volume measurement was performed in a semiautomatic segmentation method with the program "ITK-Snap."The mean volume was 2.443 cm for the right condyle and 2.278 cm for the left condyle. Bivariate analysis indicated a highly significant difference between the volume of the left and right condyles (P < 0.01). Female had a significant smaller condyle volume than male (P < 0.01 left condyle; P < 0.01 right condyle). Volume and age did not significantly correlate (P = 0.939 right condyle; P = 0.798 left condyle).A detailed assessment of the volume of mandibular condyles with cone beam computed tomography can help to assess pathophysiological alterations.Hence, the volumetric measurement may improve patient's individualized treatment.
In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to diagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions. However with the great development in automotive industry, it looks feasible today to analyze sensor's data along with machine learning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of vehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in faulty condition (when any failure in specific system has occurred) and in normal condition. The data is transmitted to the server which analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, Nearest Neighbor, and Random Forest. These patterns are later used to detect future failures in other vehicles which show the similar behavior. The approach is produced with the end goal of expanding vehicle up-time and was demonstrated on 70 vehicles of Toyota Corolla type. Accuracy comparison of all classifiers is performed on the basis of Receiver Operating Characteristics (ROC) curves.
Facial vascularized composite allotransplantation (fVCA) presents an established approach to restore form and function of patients with catastrophic facial defects. Skin is one of the target tissues of the rejection process, and due to its easy accessibility has become the gold standard in the diagnosis of rejection. Mucosal rejection frequently occurs; however, the added value of mucosal rejection assessment for patient management is unknown.
We conducted a systematic review of manuscripts listed in the MEDLINE/PubMed and GoogleScholar databases to identify articles that provide data on mucosal rejection following fVCA. For inclusion, papers had to be available as full-text and written in English. Non-VCA studies and animal studies were excluded. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.
We included 17 articles that described changes in allotransplanted mucosa of fVCAs. These articles yielded data on 168 BANFF graded biopsies of corresponding skin and mucosa biopsies. Rejection grades were consistently higher in mucosal biopsies. Concordance between allograft skin and mucosa biopsy grades increased with an increasing skin-BANFF grade. Mucosa rejection grades were on average lower in the early stages of the posttransplant period (<postoperative mo 12, time of motor, and sensory recovery) when compared to the later stages (>postoperative mo 12).
The mucosa of facial allotransplants is one of the primary targets of rejection. The data indicates that higher-grade skin rejection does not occur in absence of mucosal rejection. Further investigations are needed to elucidate the exact role of mucosal biopsies for fVCA patient management.
A precise knowledge of the condylar changes with advancing age may improve understanding of pathophysiological alterations of the mandibular condyles. However, the majority of studies focusses on morphological changes, although volumetric analysis based upon cone beam computerized tomography may provide important additional data to characterize mandibular condyles. Therefore, we aimed to provide and compare volumetric data of mandibular condyles of a young and old patient group. This is a retrospective chart review of 195 patients with cone beam computerized tomography between 2007 and 2016. Student t test, analysis of variance, and Pearson correlation test were performed to analyze associations between categorical and continuous variables. P values <0.05 were considered as significant. Volume measurement was performed in a semiautomatic segmentation method with the program 'ITK-Snap.' Side- and sex-specific significant differences between condylar volumes were found both in the young and old patient cohort. Age and posterior occlusal support did not significantly correlate with the condylar volume. Volumetric measurement of the mandibular condyles may serve as an important additional characteristic, derived from 3-dimensional imaging. Significant differences in volumetric measurement of mandibular condyles exist between sex and side, but not in relation to age and occlusal support.
Segmentation-based volume approximation holds great promise for patient individualized treatment planning and clinical management. The data suggest that maximum tumour diameter-based size characterization, especially the cuboid-formula and the maximum diameter alone, should not be recommended.
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