Statistical finite element model for bone shape and biomechanical properties

We present a framework for statistical finite element analysis combining shape and material properties, and allowing performing statistical statements of biomechanical performance across a given population. In this paper, we focus on the design of orthopaedic implants that fit a maximum percentage of the target population, both in terms of geometry and biomechanical stability. CT scans of the bone under consideration are registered non-rigidly to obtain correspondences in position and intensity between them. A statistical model of shape and intensity (bone density) is computed by means of principal component analysis. Afterwards, finite element analysis (FEA) is performed to analyse the biomechanical performance of the bones. Realistic forces are applied on the bones and the resulting displacement and bone stress distribution are calculated. The mechanical behaviour of different PCA bone instances is compared.

Accuracy and consistency of the visual diagnosis of exposed dentine on worn occlusal/incisal surfaces

Most indices for the assessment of wear of various aetiologies include the distinction between 'enamel still present' and 'dentine exposed' for grading. Since the visual diagnosis of exposed dentine has not yet been validated, the present study is a first attempt to investigate its accuracy and consistency. Sixty-one examiners (23 scientists, 18 university dentists and 20 dental students) were asked to diagnose 49 tooth areas with different grades of wear and to decide whether dentine was exposed (positive test) or not (negative test). Afterwards, the teeth were histologically evaluated. In 44 areas, dentine (also in all cases with minor wear) was exposed, and in 5 areas enamel was present. Overall sensitivity was 0.65, specificity 0.88 and the proportion of correct diagnoses was 0.67. The diagnosis 'dentine is exposed' was about 5 times as likely and the diagnosis 'dentine is not exposed' half as likely to come from an area with exposed dentine than from an enamel-covered area. The closeness of the visual diagnosis to the histological findings was only fair (kappa=0.27), no significant impact of professional experience was found. For inter- and intra-examiner agreement, kappa was 0.28 and 0.55, respectively. It was concluded that the diagnosis of exposed dentine is difficult.

MODELING FUNCTIONAL DATA WITH SPATIALLY HETEROGENEOUS SHAPE CHARACTERISTICS

We propose a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial or temporal location. We use copulas so that the marginal distributions and the dependence structure can be modeled independently. Dependence is modeled with a Gaussian or t-copula, so that there is an underlying latent Gaussian process. We model the marginal distributions using the skew t family. The mean, variance, and shape parameters are modeled nonparametrically as functions of location. A computationally tractable inferential framework for estimating heterogeneous asymmetric or heavy-tailed marginal distributions is introduced. This framework provides a new set of tools for increasingly complex data collected in medical and public health studies. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls. Using the tools we have developed, we were able to find those locations along the tract most affected by the disease. However, our methods are general and highly relevant to many functional data sets. In addition to the application to one-dimensional tract profiles illustrated here, higher-dimensional extensions of the methodology could have direct applications to other biological data including functional and structural MRI.

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Current erosion indices--flawed or valid? Summary

The problem of erosive tooth wear appears increasingly to be encountered by clinicians and researchers. An adequate way of defining and recording erosive tooth wear is essential in order to assess the extent of this clinical phenomenon, both on an individual level and in the population, and for the adequate provision of preventive and therapeutic measures. Well-established erosion indices have been used in most of these studies, although in many cases modifications have been made to suit the different research aims. This use of different indices is one reason why it still cannot be claimed that there is enough current knowledge on this clinical phenomenon. This article summarises the proceedings of a workshop to discuss the topic of dental erosion indices. The result of the workshop is the proposal for a new scoring system (Basic Erosive Wear Examination, BEWE) designed for use both within the research field and for dental clinicians, with the aims of standardising assessment of erosion for international comparisons, raising awareness and providing guidelines for treatment of erosive tooth wear in dental practice.