Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,

Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards.

This intermediate textbook on oral epidemiology is designed to meet the needs of advanced students in the fields of Dentistry and Oral Health and dentists in the early stages of their career. Readers will find detailed information on the epidemiology of individual diseases and disorders and on hot topics and methods in oral health research. The extensive first part of the book explores the international epidemiological literature regarding a wide range of conditions, from dental caries and periodontal diseases to halitosis and malocclusions. In each case, the prevalence, disease-specific measures, and associated factors are identified. Attention is then focused on cutting-edge research topics in oral epidemiology, such as the intriguing mechanisms linking oral diseases and chronic general diseases, life course epidemiology, and the role of socioeconomic determinants of oral health. The final part of the book is devoted to description of the epidemiological methods and tools applied in the field of oral health. Here, the coverage includes validation of questionnaires, data collection and data analyses, and systematic reviews and meta-analyses.

This report is the twelfth assessment of the National Institutes of Health National Research Service Awards program. The research training needs of the country in basic biomedical, clinical, and behavioral and social sciences are considered. Also included are the training needs of oral health, nursing, and health services research. The report has been broadly constructed to take into account the rapidly evolving national and international health care needs. The past and present are analyzed, and predictions with regard to future needs are presented.

This open access book examines health trajectories and health transitions at different stages of the life course, including childhood, adulthood and later life. It provides findings that assess the role of biological and social transitions on health status over time. The essays examine a wide range of health issues, including the consequences of military service on body mass index, childhood obesity and cardiovascular health, socio-economic inequalities in preventive health care use, depression and anxiety during the child rearing period, health trajectories and transitions in people with cystic fibrosis and oral health over the life course. The book addresses theoretical, empirical and methodological issues as well as examines different national contexts, which help to identify factors of vulnerability and potential resources that support resilience available for specific groups and/or populations. Health reflects the ability of individuals to adapt to their social environment. This book analyzes health as a dynamic experience. It examines how different aspects of individual health unfold over time as a result of aging but also in relation to changing socioeconomic conditions. It also offers readers potential insights into public policies that affect the health status of a population.

This book provides evidence-based guidance on the clinical applications of digital dentistry, that is, the use of dental technologies or devices that incorporate digital or computer-controlled components for the performance of dental procedures. Readers will find practically oriented information on the digital procedures currently in use in various fields of dental practice, including, for example, diagnosis and treatment planning, oral radiography, endodontics, orthodontics, implant dentistry, and esthetic dentistry. The aim is to equip practitioners with the knowledge required in order to enhance their daily practice. To this end, a problem-solving approach is adopted, with emphasis on key concepts and presentation of details in a sequential and easy to follow manner. Clear recommendations are set out, and helpful tips and tricks are highlighted. The book is written in a very readable style and is richly illustrated. Whenever appropriate, information is presented in tabular form to provide a ready overview of answers to frequent doubts and questions.

This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.