Applications of artificial intelligence in dentistry: A comprehensive review

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods: The comprehensive review was conducted in MEDLINE/ PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions: The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance: The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU

The neural foundation of moral decision-making

The nature of moral judgments has received considerable attention not only in philosophy and psychology but lately in neuroscience as well. There are two major paradigms that consider moral judgments either mainly rational, or as emotional-/ intuition-based processes. Relatively recent neuroimaging studies revealed however that both rational and emotional processes may support moral judgments. In line with these results, this doctoral thesis focused on ways that could better elucidate the supporting cognitive and/ or emotional processes of moral judgments. In a first study, moral judgments were compared to esthetic judgments by employing a whole-brain analysis. This idea was based on the philosophical and the psychological frameworks of moral sense theory and social intuitionist model respectively. Both models view moral judgments akin to esthetic judgments, as decision-making processes based on emotions/ subjective feelings. The fMRI data suggest a common denominator between the judgment modalities - a network involved in both cognitive and emotion processing. However, moral judgments seem to rely on an additional social component. In a second fMRI study, the two main paradigms of moral research were investigated. A main difference between the paradigms is the perspective the participants have towards the moral stimuli (i.e. first- or third-perspective). The fMRI data revealed that neural differences may emerge, and that they may be related to the so-called “actor-observer bias”, a tendency to attribute one’s own behavior to the situation, and the behaviors of others to their inner characteristics. Several hypotheses are put forth, which try to explain the complex neural mechanisms of moral decision-making.Die Natur moralischer Urteile hat nicht nur in der Philosophie und Psychologie, sondern neuerdings auch in den Neurowissenschaften beträchtliche Aufmerksamkeit erhalten. Es gibt zwei Haupt-Paradigmen, die moralische Urteile entweder als vorwiegend rationale, oder als emotionale und auf Intuition basierende Prozesse betrachten. Bildgebende Studien haben jedoch gezeigt, dass moralische Urteile sowohl durch rationale als auch durch emotionale Prozesse beschrieben werden können. Auf diesen Befunden aufbauend ist die vorliegende Doktorarbeit einer vertiefenden Untersuchung der zugrundeliegenden neuro-kognitiven und emotionalen Prozesse moralischer Urteile gewidmet. In einer ersten Studie wurden moralische und ästhetische Urteile durch den Einsatz einer „whole brain“ Analyse verglichen. Dieser Idee liegen philosophische und psychologische Hypothesen der „Moral Sense Theorie“ und dem „Social Intuitionist Model“ zu Grunde. Die fMRT-Daten legen einen gemeinsamen Nenner der beiden Urteilsarten nahe; es konnte ein Netzwerk identifiziert werden, das sowohl für kognitive und als auch für emotionale Verarbeitung zuständig ist. Bei moralischen Urteilen werden allerdings weitere neuronale Areale kooptiert, die eine soziale Komponente des Urteilens repräsentieren. In einer zweiten fMRT-Studie wurden zentrale Paradigmen der moralischen Forschung untersucht. Ein Hauptunterschied zwischen den Paradigmen ist die Perspektive der Teilnehmer auf die moralischen Stimuli (d.h. der ersten oder dritten Perspektive). Die fMRT-Daten legen nahe, dass Unterschiede in neuronalen Aktivierungen auf den sogenannten „Actor-Observer-Bias“ zurückgeführt werden können. Dieser Bias stellt eine Tendenz dar, das eigene Verhalten jeweils der äußeren Situation zuzuschreiben, und das Verhalten der anderen jeweils deren persönlichen Merkmalen. Auf der Grundlage neuro-kognitiver und psychologischer Hypothesen werden die komplexen neuronalen Mechanismen der moralischen Entscheidungsfindung zu erklären versucht

Specificity of Esthetic Experience for Artworks: An fMRI Study

In a previous functional magnetic resonance imaging (fMRI) study, where we investigated the neural correlates of esthetic experience, we found that observing canonical sculptures, relative to sculptures whose proportions had been modified, produced the activation of a network that included the lateral occipital gyrus, precuneus, prefrontal areas, and, most interestingly, the right anterior insula. We interpreted this latter activation as the neural signature underpinning hedonic response during esthetic experience. With the aim of exploring whether this specific hedonic response is also present during the observation of non-art biological stimuli, in the present fMRI study we compared the activations associated with viewing masterpieces of classical sculpture with those produced by the observation of pictures of young athletes. The two stimulus-categories were matched on various factors, including body postures, proportion, and expressed dynamism. The stimuli were presented in two conditions: observation and esthetic judgment. The two stimulus-categories produced a rather similar global activation pattern. Direct comparisons between sculpture and real-body images revealed, however, relevant differences, among which the activation of right antero-dorsal insula during sculptures viewing only. Along with our previous data, this finding suggests that the hedonic state associated with activation of right dorsal anterior insula underpins esthetic experience for artworks

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Embodied Robot Models for Interdisciplinary Emotion Research

Due to their complex nature, emotions cannot be properly understood from the perspective of a single discipline. In this paper, I discuss how the use of robots as models is beneficial for interdisciplinary emotion research. Addressing this issue through the lens of my own research, I focus on a critical analysis of embodied robots models of different aspects of emotion, relate them to theories in psychology and neuroscience, and provide representative examples. I discuss concrete ways in which embodied robot models can be used to carry out interdisciplinary emotion research, assessing their contributions: as hypothetical models, and as operational models of specific emotional phenomena, of general emotion principles, and of specific emotion ``dimensions''. I conclude by discussing the advantages of using embodied robot models over other models.Peer reviewe

Fractal and Polar Microstrip Antennas and Arrays for Wireless Communications

This chapter presents the research done by authors in recent years on microstrip antennas and their applications in wireless sensors network. The subject is delimited to the study of conventional microstrip antennas, from which antennas with fractal and polar shapes are proposed. A detailed description of the antenna design methodology is presented for some prototypes of microstrip antennas manufactured with different dielectric substrates. Analysis of the proposed antennas has been done through computational simulation of full-wave methods. Experimental characterization of antennas and dielectric materials has been performed with the use of a vector network analyzer. The results obtained for the resonant and radiation parameters of the antennas are presented. Computer-aided design (CAD) of microstrip antennas and arrays using fractal and polar geometrical transformations results in a wide class of antenna elements with desirable and unique characteristics, such as compact, exclusive, and esthetic antenna design for multiband or broadband frequency operation with stable radiation pattern

An Empirical Approach to the Extraction Versus Non-extraction Decision in Orthodontics

The extraction versus non-extraction decision is one of the most heavily debated topics in orthodontics. We hypothesize that orthodontic treatment planning can be enhanced by creating an empirical, evidence-based tool to aid in making this decision. To this end, we identified thresholds of overjet, overbite, and crowding that empirically determine whether or not to extract. The thresholds were combined into two prediction models: a decision tree and a logistic regression equation. These pilot models demonstrated clinical viability when tested against four borderline cases. To further improve these models, four additional models were built utilizing machine learning algorithms and an increased number of variables that influence the extraction decision, including demographics, additional clinical values, and cephalometric analysis. The best preforming model, with 81% prediction accuracy, was the convolutional neural network, which included 113 input variables. With continued development these models have potential for valuable clinical utility in the orthodontic profession.Master of Scienc

The Influence of Cutting Parameters on the Surface Quality of Routed Paper Birch and Surface Roughness Prediction Modeling

The objective of this study was to characterize the routing process to better understand the machining conditions that affect surface finish. Experiments were designed to determine the impact of cutting depth, feed speed, and grain orientation of the workpiece on the surface quality of paper birch wood. Statistical analysis showed that the cutting depth did not influence surface finish. Roughness depended greatly on feed speed and grain orientation, increasing linearly as the feed speed increased. The roughest surfaces were obtained by routing against the grain between 120 and 135° grain orientation, depending on the feed speed. Two models able to predict the surface finish based on initial cutting parameters were developed and compared. Both the statistical regression and neural network models were subjected to a validation procedure in which their performance was confirmed using data that were not used for the learning process. Results indicated that the neural network system estimates the surface roughness with less error than the statistical regression model

The brain-computer analogy "A special issue"

In this review essay, we give a detailed synopsis of the twelve contributions which are collected in a Special Issue in Frontiers Ecology and Evolution, based on the research topic "Current Thoughts on the Brain-Computer Analogy All Metaphors Are Wrong, But Some Are Useful." The synopsis is complemented by a graphical summary, a matrix which links articles to selected concepts. As first identified by Turing, all authors in this Special Issue recognize semantics as a crucial concern in the brain-computer analogy debate, and consequently address a number of such issues. What is missing, we believe, is the distinction between metaphor and analogy, which we reevaluate, describe in some detail, and offer a definition for the latter. To enrich the debate, we also deem necessary to develop on the evolutionary theories of the brain, of which we provide an overview. This article closes with thoughts on creativity in Science, for we concur with the stance that metaphors and analogies, and their esthetic impact, are essential to the creative process, be it in Sciences as well as in Arts