Arso M. Vukicevic

Mechanical weakening of devitalized teeth: three-dimensional Finite Element Analysis and prediction of tooth fracture.

AIM To determine to which extent cavity preparation and each step of dentine removal in the process of root canal treatment (access cavity preparation and root canal enlargement) both individually and jointly contribute to the weakening of the tooth. METHODS Numerical analysis using finite element method (FEM) of separate and combined influence of two-surface Class II preparation and root canal treatment was undertaken to evaluate the decrease in tooth strength. The influence of the two stages in root canal treatment, access cavity preparation and root canal enlargement, was also analysed separately and jointly. After each of these phases, the crown was restored with composite resin, and the FEA was performed only on restored teeth. To estimate the influence of all these procedures on tooth fracture resistance numerically, a Failure Index based on the maximum principal stress criterion (MPCS) was applied. Compressive and tensile stresses were analysed separately and corresponding Failure Indices were calculated. RESULTS A two-surface resin composite restoration weakened the tooth by 23.25%. Nevertheless, the Failure Indices showed that this tooth was not likely to fracture even under high occlusal stress (710N). However, after access cavity preparation, the Failure Indices reached the point where, under high occlusal force that may occur in the posterior area, a tooth fracture occurred. The enlargement of root canals had an additional, but relatively small impact on tooth weakening, making the tooth even more susceptible to fracture. The combined influence of both cavity preparation and root canal enlargement led to weakening of 62.6% under a load of 710N, ultimately causing tooth fracture. CONCLUSION The combined finite element method and the maximum principal stress analysis gave insight into the fracture mechanisms of teeth with two-surface composite restorations followed by root canal preparation. Removal of tooth tissue, despite its subsequent restoration with dental materials, weakened the tooth by changing the stress intensity and distribution through tooth structures. Access cavity preparation had the greatest influence on tooth strength whilst canal enlargement did not contribute to this process substantially.

Impact of the lower third molar presence and position on the fragility of mandibular angle and condyle: A Three-dimensional finite element study.

The aim of the present study was to investigate the influences of the presence and position of a lower third molar (M3) on the fragility of mandibular angle and condyle, using finite element analysis. From computed tomographic scans of a human mandible with normally erupted M3, two additional virtual models were generated: a mandibular model with partially impacted M3 and a model without M3. Two cases of impact were considered: a frontal and a lateral blow. The results are based on the chromatic analysis of the distributed von Mises and principal stresses, and calculation of their failure indices. In the frontal blow, the angle region showed the highest stress in the case with partially impacted M3, and the condylar region in the case without M3. Compressive stresses were dominant but caused no failure. Tensile stresses were recorded in the retromolar areas, but caused failure only in the case with partially impacted M3. In the lateral blow, the stress concentrated at the point of impact, in the ipsilateral and contralateral angle and condylar regions. The highest stresses were recorded in the case with partially impacted M3. Tensile stresses caused the failure on the ipsilateral side, whereas compressive stresses on the contralateral side.

Influence of dental restorations and mastication loadings on dentine fatigue behaviour: Image-based modelling approach

OBJECTIVES The aim of this study was to use Finite Element Analysis (FEA) to estimate the influence of various mastication loads and different tooth treatments (composite restoration and endodontic treatment) on dentine fatigue. The analysis of fatigue behaviour of human dentine in intact and composite restored teeth with root-canal-treatment using FEA and fatigue theory was performed. METHODS Dentine fatigue behaviour was analysed in three virtual models: intact, composite-restored and endodontically-treated tooth. Volumetric change during the polymerization of composite was modelled by thermal expansion in a heat transfer analysis. Low and high shrinkage stresses were obtained by varying the linear shrinkage of composite. Mastication forces were applied occlusally with the load of 100, 150 and 200N. Assuming one million cycles, Fatigue Failure Index (FFI) was determined using Goodmans criterion while residual fatigue lifetime assessment was performed using Paris-power law. RESULTS The analysis of the Goodman diagram gave both maximal allowed crack size and maximal number of cycles for the given stress ratio. The size of cracks was measured on virtual models. For the given conditions, fatigue-failure is not likely to happen neither in the intact tooth nor in treated teeth with low shrinkage stress. In the cases of high shrinkage stress, crack length was much larger than the maximal allowed crack and failure occurred with 150 and 200N loads. The maximal allowed crack size was slightly lower in the tooth with root canal treatment which induced somewhat higher FFI than in the case of tooth with only composite restoration. CONCLUSIONS Main factors that lead to dentine fatigue are levels of occlusal load and polymerization stress. However, root canal treatment has small influence on dentine fatigue. CLINICAL SIGNIFICANCE The methodology proposed in this study provides a new insight into the fatigue behaviour of teeth after dental treatments. Furthermore, it estimates maximal allowed crack size and maximal number of cycles for a specific case.

Evolutionary assembled neural networks for making medical decisions with minimal regret: Application for predicting advanced bladder cancer outcome

Abstract Development of reliable medical decision support systems has been the subject of many studies among which Artificial Neural Networks (ANNs) gained increasing popularity and gave promising results. However, wider application of ANNs in clinical practice remains limited due to the lack of a standard and intuitive procedure for their configuration and evaluation which is traditionally a slow process depending on human experts. The principal contribution of this study is a novel procedure for obtaining ANN predictive models with high performances. In order to reach those considerations with minimal user effort, optimal configuration of ANN was performed automatically by Genetic Algorithms (GA). The only two user dependent tasks were selecting data (input and output variables) and evaluation of ANN threshold probability with respect to the Regret Theory (RT). The goal of the GA optimization was reaching the best prognostic performances relevant for clinicians: correctness, discrimination and calibration. After optimally configuring ANNs with respect to these criteria, the clinical usefulness was evaluated by the RT Decision Curve Analysis. The method is initially proposed for the prediction of advanced bladder cancer (BC) in patients undergoing radical cystectomy, due to the fact that it is clinically relevant problem with profound influence on health care. Testing on the data of the ten years cohort study, which included 183 evaluable patients, showed that soft max activation functions and good calibration were the most important for obtaining reliable BC predictive models for the given dataset. Extensive analysis and comparison with the solutions commonly used in literature showed that better prognostic performances were achieved while user-dependency was significantly reduced. It is concluded that presented procedure represents a suitable, robust and user-friendly framework with potential to have wide applications and influence in further development of health care decision support systems.

Occlusal load distribution through the cortical and trabecular bone of the human mid-facial skeleton in natural dentition: A three-dimensional finite element study

Understanding of the occlusal load distribution through the mid-facial skeleton in natural dentition is essential because alterations in magnitude and/or direction of occlusal forces may cause remarkable changes in cortical and trabecular bone structure. Previous analyses by strain gauge technique, photoelastic and, more recently, finite element (FE) methods provided no direct evidence for occlusal load distribution through the cortical and trabecular bone compartments individually. Therefore, we developed an improved three-dimensional FE model of the human skull in order to clarify the distribution of occlusal forces through the cortical and trabecular bone during habitual masticatory activities. Particular focus was placed on the load transfer through the anterior and posterior maxilla. The results were presented in von Mises stress (VMS) and the maximum principal stress, and compared to the reported FE and strain gauge data. Our qualitative stress analysis indicates that occlusal forces distribute through the mid-facial skeleton along five vertical and two horizontal buttresses. We demonstrated that cortical bone has a priority in the transfer of occlusal load in the anterior maxilla, whereas both cortical and trabecular bone in the posterior maxilla are equally involved in performing this task. Observed site dependence of the occlusal load distribution may help clinicians in creating strategies for implantology and orthodontic treatments. Additionally, the magnitude of VMS in our model was significantly lower in comparison to previous FE models composed only of cortical bone. This finding suggests that both cortical and trabecular bone should be modeled whenever stress will be quantitatively analyzed.

Assessment of bone stress intensity factor using artificial neural networks

Assessment of the risks associated with bone injures is nontrivial because fragility of human bones is varying with aging. Since only a limited number of experiments have been performed on the specimens from human donors, there is limited number of fracture resistance curves available in literature. This study proposes a decision support system for the assessment of bone stress intensity factor by using artificial neural networks (ANN). The procedure estimates stress intensity factor according to patients age and diagnosed crack length. ANN was trained using the experimental data available in literature. The automated training of ANN was performed using evolutionary assembled Artificial Neural Networks. The obtained results showed good correlation with the experimental data, with potential for further improvements and applications.

Finite element analysis of uncommonly large renal arteriovenous malformation-Adjacent renal cyst complex

BACKGROUND Renal arteriovenous malformation (RAVM) represents abnormal communication between the intrarenal arterial and venous system. The purpose of this study was to investigate hemodynamics and biomechanics quantities which may influence the instability of RAVM and imply clinical complications. METHODS A detailed 3D reconstruction of RAVM was obtained from the patient CT scans, aortic inlet flow was measured by color-flow Doppler ultrasound, while material characteristics were adopted from the literature. A numerical finite element analysis (FEA) of the blood flow was performed by solving the governing equations for the viscous incompressible flow. The physical quantities calculated at the systolic and diastolic peak moment were velocity, pressure, shear stress and drag forces. RESULTS We reported a case of a 50-year-old patient with a large RAVM and adjacent renal cyst, who unsuccessfully underwent two attempts of embolization that resulted in the consequent nephrectomy. FEA showed that the cyst had a very low pressure intensity and velocity field (with unstable flow in diastolic peak). For both systolic and diastolic moments, increased values of wall shear stress were found on the places with intensive wall calcification. Unusually high values of drag force which would likely explain the presence of pressure in the cystic formation were found on the infero-medial side where the cyst wall was the thinnest and where the flow streamlines converged. CONCLUSIONS FEA showed that the hemodynamics of the cyst-RAVM complex was unstable making it prone to rupture. Clinically established diagnosis of imminent rupture together with unfavorable hemodynamics of the lesion consequently made additional attempts of embolization risky and unsuccessful leading to total nephrectomy.

Assessment of cortical bone fracture resistance curves by fusing artificial neural networks and linear regression

Abstract Bone injures (BI) represents one of the major health problems, together with cancer and cardiovascular diseases. Assessment of the risks associated with BI is nontrivial since fragility of human cortical bone is varying with age. Due to restrictions for performing experiments on humans, only a limited number of fracture resistance curves (R-curves) for particular ages have been reported in the literature. This study proposes a novel decision support system for the assessment of bone fracture resistance by fusing various artificial intelligence algorithms. The aim was to estimate the R-curve slope, toughness threshold and stress intensity factor using the two input parameters commonly available during a routine clinical examination: patients age and crack length. Using the data from the literature, the evolutionary assembled Artificial Neural Network was developed and used for the derivation of Linear regression (LR) models of R-curves for arbitrary age. Finally, by using the patient (age)-specific LR models and diagnosed crack size one could estimate the risk of bone fracture under given physiological conditions. Compared to the literature, we demonstrated improved performances for estimating nonlinear changes of R-curve slope (R2 = 0.82 vs. R2 = 0.76) and Toughness threshold with ageing (R2 = 0.73 vs. R2 = 0.66).

Assessment of Machine Learning Algorithms for the Purpose of Primary Sjögren’s Syndrome Grade Classification from Segmented Ultrasonography Images

Primary Sjogren’s syndrome (pSS) is a chronic autoimmune disease that affects primarily women (9 females/1 male). Recently, a great interest has arisen for salivary gland ultrasonography (SGUS) as a valuable tool for the assessment of major salivary gland involvement in primary Sjogren’s syndrome. The aim of this study was to assess accuracy of state of the art machine learning algorithms for the purpose of classifying pSS from SGUS images. The five-step procedure was carried out, including: image pre- processing, feature extraction, data set balancing and feature extraction, classifiers (K-Nearest Neighbour, Decision trees, Naive bayes, Discriminant analysis classifier, Random forest, Multilayer perceptron, Linear logistic regression) learning and their corresponding assessment. The preliminary results on the growing HarmonicSS cohort showed that Naive bayes (72.8% accuracy on training set, and 73.3% accuracy on test set) and Multilayer perceptron (85.0% accuracy in training stage, and 70.1% accuracy at test stage) are the most suitable for the purpose of pSS grade classification.