No description available.

BACKGROUND: Orthodontists rely heavily on cephalometric analysis to assess growth potential and direction. Geometric morphometrics examines shape and can help the clinician reach more accurate diagnoses and predict future growth. PURPOSE: The aims of this study are: 1) Determine principle components describing craniofacial shape changes; 2) Assess shape changes in growing subjects; 3) Develop a model for craniofacial growth prediction using geometric morphometrics. RESEARCH DESIGN: The Cranial base, maxilla and mandible were digitized on 330 lateral cephalograms from ages 6-16 (n=33). Generalized Procrustes analysis was performed on the longitudinal data sample. Principle Component, Discriminant Function and Two-Block Partial Least Squares analysis were assessed against changes in individual structures to determine if changes in the maxillary, mandibular or cranial base are related to changes in shape of the overall craniofacial form. RESULTS: PCA shows that the first six principle components account for 67.7 – 77.0% of the observed shape variance in each region and 56.0% of the whole form. Multivariate regression analysis predicts the shape of the entire craniofacial complex at 16 years old based on the shape observed at 6 years old with 94% certainty. An intraclass correlation coefficient of 0.98 confirms reliability. CONCLUSION: Morphometric analyses indicate that changes in maxillofacial morphology during skeletal maturation are linear. The shape of the craniofacial complex does not change significantly and growth pattern is maintained. Our model can predict the craniofacial shape at 16 years of age based on the shape observed at 6 years of age.

OBJECTIVES: Cephalometric analysis is a tool used in orthodontics for craniofacial growth assessment. Magnitude and direction of mandibular growth pose challenges that may impede successful orthodontic treatment. Accurate growth prediction enables the practitioner to improve diagnostics and orthodontic treatment planning. Deep learning provides a novel method due to its ability to analyze massive quantities of data. We compared the growth prediction capabilities of a novel deep learning algorithm with an industry-standard method. METHODS: Using OrthoDx™, 17 mandibular landmarks were plotted on selected serial cephalograms of 101 growing subjects, obtained from the Forsyth Moorrees Twin Study. The Deep Learning Algorithm (DLA) was trained for a 2-year prediction with 81 subjects. X/Y coordinates of initial and final landmark positions were inputted into a multilayer perceptron that was trained to improve its growth prediction accuracy over several iterations. These parameters were then used on 20 test subjects and compared to the ground truth landmark locations to compute the accuracy. The 20 subjects’ growth was also predicted using Ricketts’s growth prediction (RGP) in Dolphin Imaging™ 11.9 and compared to the ground truth. Mean Absolute Error (MAE) of Ricketts and DLA were then compared to each other, and human landmark detection error used as a clinical reference mean (CRM). RESULTS: The 2-year mandibular growth prediction MAE was 4.21mm for DLA and 3.28mm for RGP. DLA’s error for skeletal landmarks was 2.11x larger than CRM, while RGP was 1.78x larger. For dental landmarks, DLA was 2.79x, and Ricketts was 1.73x larger than CRM. CONCLUSIONS: DLA is currently not on par with RGP for a 2-year growth prediction. However, an increase in data volume and increased training may improve DLA’s prediction accuracy. Regardless, significant future improvements to all growth prediction methods would more accurately assess growth from lateral cephalograms and improve orthodontic diagnoses and treatment plans.

To investigate the relationship between maturation of the spheno-occipital synchondrosis (SOS) with cervical vertebrae (CVM), dental development of the canine (DI), chronologic age and intra-rater / inter-rater reliability using retrospective cone-beam computed tomography, seventy-seven subjects were randomly selected into six groups based on age and sex. Spearman correlation coefficients and tabulations between stages of maturation indices were evaluated. SOS maturation was significantly correlated with CVM and age (r > 0.8). A weaker significant correlation coefficient was found between SOS and DI (r > 0.6). All males with fused SOS were in CVM stage 4 or later, while all females were in at least CVM stage 3. No subjects with open SOS were in the post-pubertal growth spurt age group and no subjects with closed SOS were in the pre-pubertal growth spurt age group. SOS maturation showed substantial and significant inter-rater and intra-rater reliability (kappa > 0.7). / February 2015

No description available.

Software prediction is a complex area as there are no accurate models to represent reliability throughout the use of software, unlike hardware reliability. In the context of the software reliability of on-board train systems, ensuring good software reliability over time is all the more critical given the current density of rail traffic and the risk of accidents resulting from a software malfunction. This thesis proposes to use soft computing methods and historical failure data to predict the software reliability of on-board train systems. For this purpose, four machine learning models (Multi-Layer Perceptron, Imperialist Competitive Algorithm Multi-Layer Perceptron, Long Short-Term Memory Network and Convolutional Neural Network) are compared to determine which has the best prediction performance. We also study the impact of having one or more features represented in the dataset used to train the models. The performance of the different models is evaluated using the Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and the R Squared. The report shows that the Long Short-Term Memory Network is the best performing model on the data used for this project. It also shows that datasets with a single feature achieve better prediction. However, the small amount of data available to conduct the experiments in this project may have impacted the results obtained, which makes further investigations necessary. / Att förutsäga programvara är ett komplext område eftersom det inte finns några exakta modeller för att representera tillförlitligheten under hela programvaruanvändningen, till skillnad från hårdvarutillförlitlighet. När det gäller programvarans tillförlitlighet i fordonsbaserade tågsystem är det ännu viktigare att säkerställa en god tillförlitlighet över tiden med tanke på den nuvarande tätheten i järnvägstrafiken och risken för olyckor till följd av ett programvarufel. I den här avhandlingen föreslås att man använder mjuka beräkningsmetoder och historiska data om fel för att förutsäga programvarans tillförlitlighet i fordonsbaserade tågsystem. För detta ändamål jämförs fyra modeller för maskininlärning (Multi-Layer Perceptron, Imperialist Competitive Algorithm Mult-iLayer Perceptron, Long Short-Term Memory Network och Convolutional Neural Network) för att fastställa vilken som har den bästa förutsägelseprestandan. Vi undersöker också effekten av att ha en eller flera funktioner representerade i den datamängd som används för att träna modellerna. De olika modellernas prestanda utvärderas med hjälp av medelabsolut fel, medelkvadratfel, rotmedelkvadratfel och R-kvadrat. Rapporten visar att Long Short-Term Memory Network är den modell som ger bäst resultat på de data som använts för detta projekt. Den visar också att dataset med en enda funktion ger bättre förutsägelser. Den lilla mängd data som fanns tillgänglig för att genomföra experimenten i detta projekt kan dock ha påverkat de erhållna resultaten, vilket gör att ytterligare undersökningar är nödvändiga.