Background: Dentinogenesis imperfecta type I (DGI-I) is a hereditary alteration of dentin associated with osteogenesis imperfecta (OI). Aim: To describe and study the morphological characteristics of DGI-I with scanning electron microscopy (SEM). Material and methods: Twenty-five teeth from 17 individuals diagnosed with OI and 30 control samples were studied with SEM at the level of the enamel, dentin–enamel junction (DEJ) and four levels of the dentin, studying its relationship with clinical–radiographic alterations. The variables were analysed using Fisher’s exact test, with a confidence level of 95% and asymptotic significance. Results: OI teeth showed alterations in the prismatic structure in 56%, interruption of the union in the enamel and dentin in 64% and alterations in the tubular structure in all of the cases. There is a relationship between the severity of OI and the morphological alteration of the dentin in the superficial (p = 0.019) and pulpar dentin (p 0.004) regions. Conclusions: Morphological alterations of the tooth structure are found in OI samples in the enamel, DEJ and dentin in all teeth regardless of the presence of clinical–radiographic alterations. Dentin structural anomalies and clinical dental alterations were observed more frequently in samples from subjects with a more severe phenotype of OI.

Aim: A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular Index, Mandibular Ratio, Mandibular Cortical Index) in order to detect the thinning and deterioration of the mandibular bone. Data can be stored for posterior massive analysis. Methods: Panoramic radiographies are intrinsically complex, including: artificial structures, unclear limits in bony structures, jawbones with irregular curvatures and intensity levels, irregular shapes and borders of the mental foramen, irregular teeth alignments or missing dental pieces. An intelligent sequence of linked imaging segmentation processes is proposed to cope with the above situations towards the design of the automatic segmentation, making the following contributions: (i) Fuzzy K-means classification for identifying artificial structures; (ii) adjust a tangent line to the lower border of the lower jawbone (lower cortex), based on texture analysis, grey scale dilation, binarization and labelling; (iii) identification of the mental foramen region and its centre, based on multi-thresholding, binarization, morphological operations and labelling; (iv) tracing a perpendicular line to the tangent passing through the centre of the mental foramen region and two parallel lines to the tangent, passing through borders on the mental foramen intersected by the perpendicular; (v) following the perpendicular line, a sweep is made moving up the tangent for detecting accumulation of binary points after applying adaptive filtering; (vi) detection of the lower mandible alveolar crest line based on the identification of inter-teeth gaps by saliency and interest points feature description. Results: The performance of the proposed approach was quantitatively compared against the criteria of expert dentists, verifying also its validity with statistical studies based on the analysis of deterioration of bone structures with different levels of osteoporosis. All indices are computed inside two regions of interest, which tolerate flexibility in sizes and locations, making this process robust enough. Conclusions: The proposed approach provides an automatic procedure able to process with efficiency and reliability panoramic X-Ray images for early osteoporosis detection.

The failure of composite resin restorations in the posterior region is an ongoing concern in current clinical practice This study assesses possible factors and causes of the failure of restoration 1 year after their placement by fourth year dental students (on a 5-year degree course). While the systematic assessment of dental students does not appear to have received much attention in the field, this study asserts the need and benefit of an assessment methodology that can (1) predict the success or failure of restorations placed by dental students and (2) assist the clinical instructor in identifying the performance profile of each student. Eighty-one patients aged 26–77 years were treated by 81 undergraduates in a prospective cohort study from November 2013 to December 2015. One year after treatment, restorations were assessed by the same staff member who acted as the supervisor during the restoration placement. A CBR system was applied to make predictions about restorations. The CBR includes different machine learning techniques and statistical tests in the CBR cycle. The system calculates the relevant variables, which are used to predict failures. The accuracy of the system is measured with the AUC and the accuracy. The AUC obtained is 0.935 while the Kappa index and the accuracy are 91.36 and 0.75, respectively. In conclusion, factors related to the patient and to the treatment are associated to the failure of the restorative treatment. Of particular interest, the CBR was useful for the performance of a predictive model to estimate the probability of failure of resin restorations placed by students.

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.

The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.

ObjectivesTo compare the 6-month clinical performance of a "universal" adhesive in non-carious Class V lesions using four different adhesive strategies. Materials and Methods21 patients participated in this study, in which 70 Class V restorations were placed.The restorations were randomly assigned into four experimental groups according to different adhesive strategies of Scotchbond Universal Adhesive (SBU, 3M ESPE): A. 3-step etch-andrinse: 34% phosphoric acid (PA, Scotchbond Universal Etchant, 3M ESPE) and application of SBU followed by one coat of the non-solvated bonding resin Scotchbond Multi-Purpose Adhesive (SBMPA, 3M ESPE); B. 2-step etch-and-rinse: 34% PA followed by SBU; C. 2-step selfetch: SBU followed by one coat of SBMPA; D. 1-step self-etch: SBU alone.All restorations were evaluated at baseline and after 6 months by two blind observers using the USPHS criteria.Statistical analysis was performed with the non-parametrical tests Kruskal-Wallis, Mann Whitney U and Wilcoxon (p<0.05). ResultsOnly one restoration from the group 1-step/SBU was lost at six months.Marginal adaptation was the only criterion for which statistically worse scores were measured after 6 months (p<0.01).Significantly more bravo scores were detected when SBU was used following a self-etch strategy.The restorations performed with SBU as 1-step self-etch adhesive exhibited a significantly deterioration of the marginal adaptation after 6 months. ConclusionsRestorations performed with SBU under a self-etch strategy showed worse marginal adaptation after 6 months of clinical use compared to those with SBU under an etch-and-rinse strategy.The addition of a nonsolvated hydrophobic coating (SBMPA) did not influence the clinical performance.

Schools and universities face multiple challenges when they target initiating or expanding undergraduate programs. Education has traditionally utilized a teacher-centered educational and assessment approach. Only few attempts exist to involve objective feedback and non-traditional assessment methods and technologies to improve the processes of teaching, learning, and education in general.This paper addresses a novel objective multi-parameter assessment methodology based on Soft computing technology to discover the effect of students' groupings by exploiting the interrelationships between the grades the students received for their laboratory subjects and the grade they obtained in the university enrolment exam. The research results allow for exploring non-desirable discordant teaching and assessment practices for individuals or groups. In addition, the results obtained illustrate opportunities to focus on the individual student during the education process and determine adaptive teaching strategies based on the particular level of knowledge and experience. Toward these results statistical and Soft computing models implementing Unsupervised Neural and Exploratory Projection Techniques have been applied to carry on the objective assessment of the students' skills development during the entire higher education period.Empirical verification of the proposed assessment model is performed in a real environment, where a case study is defined, and analysed. The real data set to validate the performance of the proposed approach has been collected at the School of Dentistry of the Complutense University of Madrid.

This multidisciplinary study presents the application of two well known soft computing methods – flexible neural trees, and evolutionary fuzzy rules – for the prediction of the error parameter between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

This study presents a novel soft computing procedure based on the application of artificial neural networks, genetic algorithms and identification systems, which makes it possible to optimise the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving both time and financial costs and/or energy. This novel intelligent procedure is based on the following phases. Firstly, a neural model extracts the internal structure and the relevant features of the data set representing the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques. This constitutes the model for the fitness function of the production process, using relevant features of the data set. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The proposed novel approach was tested under real dental milling processes using a high-precision machining centre with five axes, requiring high finishing precision of measures in micrometres with a large number of process factors to analyse. The results of the experiment, which validate the performance of the proposed approach, are presented in this study.

Within the field of odontology, an analysis of the probability of success of endodontic retreatment facilitates the diagnostic and decision-making process of medical personnel. This study presents a case-based reasoning system that predicts the probability of success and failure of retreatments to avoid extraction. Different classifiers were applied during the reuse phase of the case-based reasoning process. The system was tested on a set of patients who received retreatments, and a set of variables considered to be of particular interest, were selected.

This multidisciplinary study presents the application of two soft computing methods utilizing the artificial evolution of symbolic structures – evolutionary fuzzy rules and flexible neural trees – for the prediction of dental milling time-error, i.e. the error between real dental milling time and forecast given by the dental milling machine. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

This study presents a novel hybrid intelligent system which focuses on the optimisation of machine parameters for dental milling purposes based on the following phases. Firstly, an unsupervised neural model extracts the internal structure of a data set describing the model and also the relevant features of the data set which represents the system. Secondly, the dynamic system performance of different variables is specifically modelled using a supervised neural model and identification techniques from relevant features of the data set. This model constitutes the goal function of the production process. Finally, a genetic algorithm is used to optimise the machine parameters from a non parametric fitness function. The reliability of the proposed novel hybrid system is validated with a real industrial use case, based on the optimisation of a high-precision machining centre with five axes for dental milling purposes.

PURPOSE:To evaluate the mechanical properties of occlusal wear lesions identified as sclerotic.METHODS:Nanoindentation and nanoscratch techniques have been applied to determine elastic modulus (E), hardness (H) and wear resistance of different types of sclerotic dentin. Nanoscratch testing was applied to evaluate the tribological behavior. Mechanical properties of sclerotic dentin were evaluated together with scanning electron micrographs to show the different degree of tubular occlusion.RESULTS:The higher the degree of sclerosis, the lower were the measured mechanical properties. The highest values (E = 20 GPa and H = 0.67 GPa) were obtained in normal dentin, and the lowest (E = 11.4 GPa and H = 0.51 GPa) in severe sclerotic lesions. These differences were statistically significant (P<0.05). The groove width in the nanoscratch tests was also higher for moderate or severe sclerosis than for normal dentin.

A novel soft computing system to optimize a dental milling process is proposed. The model is based on the initial application of several statistical and projection methods as Principal Component Analysis and Cooperative Maximum Likelihood Hebbian Learning to analyze the structure of the data set and to identify the most relevant variables. Finally, a supervised neural model and identification techniques are applied, in order to model the process and optimize it. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

A novel bio-inspired computational high-precision dental milling system is proposed in this interdisciplinar research. The system applies several bio-inspired models, based on unsupervised learning, that analyse and identify the most relevant features of high-precision dental-milling data sets and their internal structures. Finally, a supervised neural architecture and certain identification techniques are applied, in order to model and to optimize the high-precision process. This is done by empirically testing the model using a real data set taken from a dynamic high-precision machining centre with five axes.

El Sistema Central es un rejuvenecimiento alpino del zócalo hercínico de la Placa Ibérica. En su borde meridional afloran las terminaciones hacia el continente de las plataformas carbonatadas cretácicas del Surco Ibérico. Los depósitos cretácicos, en las proximidades de la Cordillera Ibérica (NE) son carbonatados, de ambientes litorales y marinos someros; mientras que hacia el interior de la Placa Ibérica, pasan a depósitos terrígenos, de medios litorales y continentales. Hay también una importante reducción de los espesores en esa misma dirección. El registro estratigráfico se organiza en dieciséis unidades litoestratigráficas y en ocho secuencias deposicionales, que se correlacionan con la carta de ciclos globales de Haq et al. Los materiales estudiados abarcan desde el Cenomanense al Campaniense. La estructura de apilamiento es agradacional, con un onlap poco marcado en la base, una truncación y una discordancia interna en el Santoniense. Las variaciones de espesor y de facies son controladas por fracturas sinsedimentarias, en un marco distensivo. Ellas tienen poco salto y son más modernas hacia el exterior de la cuenca sedimentaria.