Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Guideline on therapeutic dentistry for the 5-th term

РУКОВОДСТВАСТОМАТОЛОГИЯ ЛЕЧЕБНО-ВОССТАНОВИТЕЛЬНАЯСТОМАТОЛОГИЯ ТЕРАПЕВТИЧЕСКАЯИНОСТРАННЫЕ СТУДЕНТЫУЧЕБНО-МЕТОДИЧЕСКИЕ ПОСОБИЯПособие составлено в соответствии с учебной программой для медицинских вузов по терапевтической стоматологии. Предназначено для внутреннего использования

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience

White matter fibres dissection in the human brain

PhD ThesisIntroduction: lesion to white matter fibres can induce permanent neurological deficits due to the induction of disconnection syndromes. Knowledge of white matter fibre anatomy is therefore relevant to the neurosurgeon in order to minimise the risk of causing neurological damage when approaching lesions in eloquent areas of the brain. Aim: to investigate the 3D anatomy of white matter fibres with particular attention to the associative tracts, including short arcuate fibres and intralobar fibres. The results obtained will be used to provide insights in brain connectivity, delineating networks important for specific brain functions. Methods: The Klingler technique for white matter dissection was followed. Brain specimens were collected and prepared at the Newcastle Brain Tissue Resource, Newcastle University. Brains were initially fixed in 10% formalin for at least 4 weeks. After removing the pia-mater and arachnoid, the brains were frozen at -15C° for 2 weeks. The water crystallisation induced by the freezing process separates the white matter fibres, facilitating the dissection of the tracts. Dissection was performed with wooden spatulas and blunt metallic dissectors, removing the cortex and exposing the white matter. The short associative (U-shaped) fibres were initially exposed. Long associative, commissural and projection fibres were demonstrated as the dissection proceeded. Results: five papers form the main body of the present work: 1) “Raymond de Vieussens and his contribution to the study of white matter anatomy”. This historical paper reviewed the history of white matter dissection, focusing on the work of Raymond de Vieussens, who gave the first account of the centrum ovale and of the continuity of the corticospinal tract from the centrum ovale to the brainstem. 2) “The white matter of the human cerebrum: part I The occipital lobe by Heinrich Sachs “ ; 3) “Intralobar fibres of the occipital lobe: A post mortem dissection study”. These joint papers were dedicated to the white matter anatomy of the occipital lobe. A rich network of association fibres, arranged around the ventricular wall, was demonstrated. A new white matter tract, connecting the cuneus to the lingula, was also described. Our original data I II were compared to the atlas of occipital fibres produced by the German anatomist Heinrich Sachs. 4) “White matter connections of the Supplementary Motor Area (SMA) in humans”. This study demonstrated that the SMA shows a wide range of connections with motor, language and limbic areas. Features of the SMA syndrome (akinesia and mutism) can be better understood on the basis of these findings. 5) “Anatomical connections of the Subgenual Cingulate Region” (SCG). This study showed that the SCG is at the centre of a large network, connecting prefrontal, limbic and mesotemporal regions. The connectivity of this region can help explain the clinical effect of neuromodulaton of the SCG in Deep Brain Stimulation for neuropsychiatric disorders. Conclusions: Klingler dissection provided original data about the connections of different brain regions that are relevant to neurosurgical practice, along with the description of a new white matter tract, connecting the cuneus to the lingula

Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community

Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper

Cortical mapping of the neuronal circuits modulating the muscle tone. Introduction to the electrophysiological treatment of the spastic hand

L'objectiu d'aquest estudi es investigar l'organització cortical junt amb la connectivitat còrtico-subcortical en subjectes sans, com a estudi preliminar. Els mapes corticals s'han fet per TMS navegada, i els punts motors obtinguts s'han exportant per estudi tractogràfic i anàlisi de las seves connexions. El coneixement precís de la localització de l'àrea cortical motora primària i les seves connexions es la base per ser utilitzada en estudis posteriors de la reorganització cortical i sub-cortical en pacients amb infart cerebral. Aquesta reorganització es deguda a la neuroplasticitat i pot ser influenciada per els efectes neuromoduladors de la estimulació cerebral no invasiva.The purpose of this study is to investigate the motor cortex organisation together with the cortico-subcortical connectivity in healthy subjects, as a preliminary study. Cortical maps have been performed by navigated TMS and the motor points have been exported to DTI to study their subcortical connectivity. The precise knowledge of localization of the primary motor cortex area and its connectivity is the base to be used in later studies of cortical and subcortical re-organisation in stroke patients. This re-organisation is due to the neuroplascity and can be influenced by the neuromodulation effects of the non-invasive cerebral stimulation therapy by TMS

Oral and Maxillofacial Surgery

Oral and maxillofacial surgery is a specialized branch of dentistry that deals with the surgical management of various head and neck pathologies. The specialty focuses on reconstructive surgery of the oro-facial region, surgery of facial trauma, the oral cavity and jaws, dental implants as well as cosmetic surgery. As such, surgeons in this field require extensive knowledge of not only these various surgical procedures but also head and neck anatomy. This book provides comprehensive information on both. Its goal is to educate oral and maxillofacial surgeons to enable them to treat a wide range of conditions and diseases using the most current surgical trends

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification