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

Three-dimensional modeling of the human jaw/teeth using optics and statistics.

Object modeling is a fundamental problem in engineering, involving talents from computer-aided design, computational geometry, computer vision and advanced manufacturing. The process of object modeling takes three stages: sensing, representation, and analysis. Various sensors may be used to capture information about objects; optical cameras and laser scanners are common with rigid objects, while X-ray, CT and MRI are common with biological organs. These sensors may provide a direct or an indirect inference about the object, requiring a geometric representation in the computer that is suitable for subsequent usage. Geometric representations that are compact, i.e., capture the main features of the objects with a minimal number of data points or vertices, fall into the domain of computational geometry. Once a compact object representation is in the computer, various analysis steps can be conducted, including recognition, coding, transmission, etc. The subject matter of this dissertation is object reconstruction from a sequence of optical images using shape from shading (SFS) and SFS with shape priors. The application domain is dentistry. Most of the SFS approaches focus on the computational part of the SFS problem, i.e. the numerical solution. As a result, the imaging model in most conventional SFS algorithms has been simplified under three simple, but restrictive assumptions: (1) the camera performs an orthographic projection of the scene, (2) the surface has a Lambertian reflectance and (3) the light source is a single point source at infinity. Unfortunately, such assumptions are no longer held in the case of reconstruction of real objects as intra-oral imaging environment for human teeth. In this work, we introduce a more realistic formulation of the SFS problem by considering the image formation components: the camera, the light source, and the surface reflectance. This dissertation proposes a non-Lambertian SFS algorithm under perspective projection which benefits from camera calibration parameters. The attenuation of illumination is taken account due to near-field imaging. The surface reflectance is modeled using the Oren-Nayar-Wolff model which accounts for the retro-reflection case. In this context, a new variational formulation is proposed that relates an evolving surface model with image information, taking into consideration that the image is taken by a perspective camera with known parameters. A new energy functional is formulated to incorporate brightness, smoothness and integrability constraints. In addition, to further improve the accuracy and practicality of the results, 3D shape priors are incorporated in the proposed SFS formulation. This strategy is motivated by the fact that humans rely on strong prior information about the 3D world around us in order to perceive 3D shape information. Such information is statistically extracted from training 3D models of the human teeth. The proposed SFS algorithms have been used in two different frameworks in this dissertation: a) holistic, which stitches a sequence of images in order to cover the entire jaw, and then apply the SFS, and b) piece-wise, which focuses on a specific tooth or a segment of the human jaw, and applies SFS using physical teeth illumination characteristics. To augment the visible portion, and in order to have the entire jaw reconstructed without the use of CT or MRI or even X-rays, prior information were added which gathered from a database of human jaws. This database has been constructed from an adult population with variations in teeth size, degradation and alignments. The database contains both shape and albedo information for the population. Using this database, a novel statistical shape from shading (SSFS) approach has been created. Extending the work on human teeth analysis, Finite Element Analysis (FEA) is adapted for analyzing and calculating stresses and strains of dental structures. Previous Finite Element (FE) studies used approximate 2D models. In this dissertation, an accurate three-dimensional CAD model is proposed. 3D stress and displacements of different teeth type are successfully carried out. A newly developed open-source finite element solver, Finite Elements for Biomechanics (FEBio), has been used. The limitations of the experimental and analytical approaches used for stress and displacement analysis are overcome by using FEA tool benefits such as dealing with complex geometry and complex loading conditions

A dynamical approach to gestural patterning in speech production.

In this article, we attempt to reconcile the linguistic hypothesis that speech involves an underlying sequencing of abstract, discrete, context-independent units, with the empirical observation of continuous, context-dependent interleaving of articulatory movements. To this end, we first review a previously proposed task-dynamic model for the coordination and control of the speech articulators. We then describe an extension of this model in which invariant speech units (gestural primitives) are identified with context-independent sets of parameters in a dynamical system having two functionally distinct but interacting levels. The intergesturallevel is defined according to a set of activation coordinates; the interarticulator level is defined according to both model articulator and tract-variable coordinates. In the framework of this extended model, coproduction effects in speech are described in terms of the blending dynamics defined among a set of temporally overlapping active units; the relative timing of speech gestures is formulated in terms of the serial dynamics that shape the temporal patterning of onsets and offsets in unit activations. Implications of this approach for certain phonological issues are discussed, and a range of relevant experimental data on speech and limb motor control is reviewed

ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION

Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system

Computer lipreading via hybrid deep neural network hidden Markov models

Constructing a viable lipreading system is a challenge because it is claimed that only 30% of information of speech production is visible on the lips. Nevertheless, in small vocabulary tasks, there have been several reports of high accuracies. However, investigation of larger vocabulary tasks is rare. This work examines constructing a large vocabulary lipreading system using an approach based-on Deep Neural Network Hidden Markov Models (DNN-HMMs). We present the historical development of computer lipreading technology and the state-ofthe-art results in small and large vocabulary tasks. In preliminary experiments, we evaluate the performance of lipreading and audiovisual speech recognition in small vocabulary data sets. We then concentrate on the improvement of lipreading systems in a more substantial vocabulary size with a multi-speaker data set. We tackle the problem of lipreading an unseen speaker. We investigate the effect of employing several stepstopre-processvisualfeatures. Moreover, weexaminethecontributionoflanguage modelling in a lipreading system where we use longer n-grams to recognise visual speech. Our lipreading system is constructed on the 6000-word vocabulary TCDTIMIT audiovisual speech corpus. The results show that visual-only speech recognition can definitely reach about 60% word accuracy on large vocabularies. We actually achieved a mean of 59.42% measured via three-fold cross-validation on the speaker independent setting of the TCD-TIMIT corpus using Deep autoencoder features and DNN-HMM models. This is the best word accuracy of a lipreading system in a large vocabulary task reported on the TCD-TIMIT corpus. In the final part of the thesis, we examine how the DNN-HMM model improves lipreading performance. We also give an insight into lipreading by providing a feature visualisation. Finally, we present an analysis of lipreading results and suggestions for future development

Optical Diagnostics in Human Diseases

Optical technologies provide unique opportunities for the diagnosis of various pathological disorders. The range of biophotonics applications in clinical practice is considerably wide given that the optical properties of biological tissues are subject to significant changes during disease progression. Due to the small size of studied objects (from μm to mm) and despite some minimum restrictions (low-intensity light is used), these technologies have great diagnostic potential both as an additional tool and in cases of separate use, for example, to assess conditions affecting microcirculatory bed and tissue viability. This Special Issue presents topical articles by researchers engaged in the development of new methods and devices for optical non-invasive diagnostics in various fields of medicine. Several studies in this Special Issue demonstrate new information relevant to surgical procedures, especially in oncology and gynecology. Two articles are dedicated to the topical problem of breast cancer early detection, including during surgery. One of the articles is devoted to urology, namely to the problem of chronic or recurrent episodic urethral pain. Several works describe the studies in otolaryngology and dentistry. One of the studies is devoted to diagnosing liver diseases. A number of articles contribute to the studying of the alterations caused by diabetes mellitus and cardiovascular diseases. The results of all the presented articles reflect novel innovative research and emerging ideas in optical non-invasive diagnostics aimed at their wider translation into clinical practice

Registration and statistical analysis of the tongue shape during speech production

This thesis analyzes the human tongue shape during speech production. First, a semi-supervised approach is derived for estimating the tongue shape from volumetric magnetic resonance imaging data of the human vocal tract. Results of this extraction are used to derive parametric tongue models. Next, a framework is presented for registering sparse motion capture data of the tongue by means of such a model. This method allows to generate full three-dimensional animations of the tongue. Finally, a multimodal and statistical text-to-speech system is developed that is able to synthesize audio and synchronized tongue motion from text.Diese Dissertation beschäftigt sich mit der Analyse der menschlichen Zungenform während der Sprachproduktion. Zunächst wird ein semi-überwachtes Verfahren vorgestellt, mit dessen Hilfe sich Zungenformen von volumetrischen Magnetresonanztomographie- Aufnahmen des menschlichen Vokaltrakts schätzen lassen. Die Ergebnisse dieses Extraktionsverfahrens werden genutzt, um ein parametrisches Zungenmodell zu konstruieren. Danach wird eine Methode hergeleitet, die ein solches Modell nutzt, um spärliche Bewegungsaufnahmen der Zunge zu registrieren. Dieser Ansatz erlaubt es, dreidimensionale Animationen der Zunge zu erstellen. Zuletzt wird ein multimodales und statistisches Text-to-Speech-System entwickelt, das in der Lage ist, Audio und die dazu synchrone Zungenbewegung zu synthetisieren.German Research Foundatio

Sox10 regulates enteric neural crest cell migration in the developing gut

Concurrent Sessions 1: 1.3 - Organs to organisms: Models of Human Diseases: abstract no. 1417th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, VII Latin American Society of Developmental Biology Meeting and XI Congreso de la Sociedad Mexicana de Biologia del Desarrollo. The Conference's web site is located at http://www.inb.unam.mx/isdb/Sox10 is a HMG-domain containing transcription factor which plays important roles in neural crest cell survival and differentiation. Mutations of Sox10 have been identified in patients with Waardenburg-Hirschsprung syndrome, who suffer from deafness, pigmentation defects and intestinal aganglionosis. Enteric neural crest cells (ENCCs) with Sox10 mutation undergo premature differentiation and fail to colonize the distal hindgut. It is unclear, however, whether Sox10 plays a role in the migration of ENCCs. To visualize the migration behaviour of mutant ENCCs, we generated a Sox10NGFP mouse model where EGFP is fused to the N-terminal domain of Sox10. Using time-lapse imaging, we found that ENCCs in Sox10NGFP/+ mutants displays lower migration speed and altered trajectories compared to normal controls. This behaviour was cell-autonomous, as shown by organotypic grafting of Sox10NGFP/+ gut segments onto control guts and vice versa. ENCCs encounter different extracellular matrix (ECM) molecules along the developing gut. We performed gut explant culture on various ECM and found that Sox10NGFP/+ ENCCs tend to form aggregates, particularly on fibronectin. Time-lapse imaging of single cells in gut explant culture indicated that the tightly-packed Sox10 mutant cells failed to exhibit contact inhibition of locomotion. We determined the expression of adhesion molecule families by qPCR analysis, and found integrin expression unaffected while L1-cam and selected cadherins were altered, suggesting that Sox10 mutation affects cell adhesion properties of ENCCs. Our findings identify a de novo role of Sox10 in regulating the migration behaviour of ENCCs, which has important implications for the treatment of Hirschsprung disease.postprin

Analysis of craniofacial defects in Six1/Eya1-associated Branchio-Oto-Renal Syndrome

Poster Session I - Morphogenesis: 205/B10117th ISDB 2013 cum 72nd Annual Meeting of the Society for Developmental Biology, 7th Latin American Society of Developmental Biology Meeting and 11th Congreso de la Sociedad Mexicana de Biologia del Desarrollo.Branchio-Oto-Renal (BOR) syndrome patients exhibit craniofacial and renal anomalies as well as deafness. BOR syndrome is caused by mutations in Six1 or Eya1, both of which regulate cell proliferation and differentiation. The molecular mechanism underlying the craniofacial and branchial arch (BA) defects in BOR syndrome is unclear. We have found that Hoxb3 is up-regulated in the second branchial arch (BA2) of Six1-/- mutants. Moreover, Hoxb3 over-expression in transgenic mice leads to BA abnormalities which are similar to the BA defects in Six1-/- or Eya1-/- mutants, suggesting a regulatory relationship among Six1, Eya1 and Hoxb3 genes. The aim of this study is to investigate the molecular mechanism underlying abnormal BA development in BOR syndrome using Six1 and Eya1 mutant mice. Two potential Six1 binding sites were identified on the Hoxb3 gene. In vitro and in vivo Chromatin IP assays showed that Six1 could directly bind to one of the sites specifically. Furthermore, using a chick in ovo luciferase assay we showed that Six1 could suppress gene expression through one of the specific binding sites. On the other hand, in Six1-/- mutants, we found that the Notch ligand Jag1 was up-regulated in BA2. Similarly, in Hoxb3 transgenic mice, ectopic expression of Jag1 could be also detected in BA2. To investigate the activation of Notch signaling pathway, we found that Notch intracellular domain (NICD), a direct indicator of Notch pathway activation, was up-regulated in BAs of Six1-/-; Eya1-/- double mutants. Our results indicate that Hoxb3 and Notch signaling pathway are involved in mediating the craniofacial defects of Six1/Eya1-associated Branchio-Oto-Renal Syndrome.postprin