Dispositivo de Deteção do Bruxismo do Sono

This thesis aims to explore and, ultimately, develop a system capable of monitoring physiological signals to detect bruxism events. Bruxism is a disorder characterized by the habit of pressing and grinding the teeth. These events can either occur during the day (Awake Bruxism) or during the night (Sleep Bruxism). Studies suggest that 20% of the adult population suffer from Awake Bruxism, and 8-16% from Sleep Bruxism. The consequences of this disorder are several, ranging from tooth wear, dental fractures, or abfraction, resulting in headaches, or facial myalgia. This dissertation focuses on the Sleep Bruxism type since it’s harder to detect and treat. First, a study about the evolution of technology in healthcare is carried out, fundamentally about how it was introduced and how did it get to the point it is now. The topic of wearable devices is also explored, in the sense that it’s where the market is going and how these devices can transform healthcare. Then, the study converges on the devices developed especially for bruxism, namely which devices, and what type of techniques are used. Subsequently, the general concept for the system is elaborated, exploring several options both in terms of devices and physiological data to be parameterized. However, some restrictions exist for the construction of the system. For the construction of an intraoral system, the device has to be of small dimensions and with low energy consumption. With these constraints, the system has implemented an Inertial Measurement Unit to estimate the orientation of the patient’s sleeping position, and force sensors to measure the force exerted between the teeth. For compactness, a Systemon-Chip is used, since it includes an ARM Cortex M4 processor, several peripherals, and an RF transceiver in one package. The system is not only responsible for the data acquisition, but also the data transmission. This is accomplished by using Bluetooth Low Energy, which is one of the most common protocols for low-power devices. Customized service is developed for this purpose, consisting of three different characteristics: the force characteristic, the accelerometer characteristic, and the gyroscope characteristic. The reason is for maximizing efficiency. The last step was to develop the prototype, testing its functionalities and try to project next iterations of the prototype

Machine Learning Assisted 5-Part Tooth Segmentation Method for CBCT-Based Dental Age Estimation in Adults

Background: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG5t) method. Additionally, supervised machine learning modelling —namely support vector regression (SVR) with linear and polynomial kernel, and regression tree — was tested and compared with the multiple linear regression model.Material and Methods: CBCT scans from 99 patients aged between 20 to 59.99 were collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study.  Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age. Results: No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel (R2adj = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean absolute error and 6.05 years of root means squared error. However, SG5t demands a complex approach to segment the enamel volume in the crown section and a lengthier labor time of 45 minutes per tooth

Machine Learning Assisted 5-Part Tooth Segmentation Method for CBCT-Based Dental Age Estimation in Adults

Background: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG5t) method. Additionally, supervised machine learning modelling —namely support vector regression (SVR) with linear and polynomial kernel, and regression tree — was tested and compared with the multiple linear regression model.Material and Methods: CBCT scans from 99 patients aged between 20 to 59.99 were collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study.  Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age. Results: No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel (R2adj = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean absolute error and 6.05 years of root means squared error. However, SG5t demands a complex approach to segment the enamel volume in the crown section and a lengthier labor time of 45 minutes per tooth

Computational Modeling of Fracture Failure in Mineralized and Prosthetic Biomaterials

Natural mineralized tissues, e.g., teeth and bone, have the capacity to tolerate the daily physiological loading. However, due to their high mineralized composition, they have been recognized as a class of relatively brittle biomaterials. The inherent brittle nature and fairly high susceptibility to mechanical failure present a more critical problem in biomedical research field. To replace such diseased or damaged mineralized tissues, prosthetic materials are largely applied in the areas of dental and osteo clinical treatments. Ceramic materials provide numerous favourable characteristics, including biocompatibility and chemical resistance. In addition to the dental industry, applications of osteofixation/osteosynthiesis devices are considered fundamental to stabilize various treatments of bone defects for promoting osteointegration and reconstruction. However, clinical observations and specialized literature have revealed that dental restorative materials and prosthetic fixation devices are often subject to high stress, leading to fracture either by catastrophic overloading or cyclic fatigue failure. The aim of this thesis is to develop a computational modelling framework on the basis of the extended finite element method (XFEM) to investigate the fracture behaviors of mineralised and synthetic biomaterials in various medical applications. The XFEM modelling results are validated by being compared with the in-vitro experiments and/or clinical observations. Through the research in this thesis studies, XFEM has been demonstrated to be a powerful tool to analyse fracture behaviors in the bio-structures subjected to not only static loadings but also cyclic loadings. The outcomes generated in this thesis help gain some insightful understanding failure of the native or prosthetic structures, which is anticipated to provide some clinical guidelines for the design optimisation of patient-specific prosthetic devices to ensure their reliability and longevity

Effets d'un appareil d'avancement mandibulaire calibré sur le bruxisme relié au sommeil

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Mastication in jaw muscle pain

Background: Integrated Pain Adaptation Models suggest a possible pain-motor interaction. Mechanisms affecting the jaw muscle spindles seem to affect the ability to bite and chew, suggesting that jaw muscle pain may be a potential modifier of mastication in humans. Objectives: The general objective of this doctoral thesis was to investigate the mastication performance in patients with painful temporomandibular disorders (TMD) and, more specifically, clinical chronic pain within the masticatory muscles. Study I investigated the effects of chronic and acute jaw muscle pain on oral motor control during precision biting. Study II focused on the optimisation of excessive gum chewing as an experimental model to induce jaw muscle pain and fatigue similar to that seen in painful TMD. Study III focused on chewing performance in TMD patients with myalgia. Methods and Results: Study I involved a comparison of patients with chronic masseter muscle pain and healthy participants. Experimental acute pain was induced by bilateral, simultaneous sterile hypertonic saline infusions into the healthy masseter muscles. A standardised hold and split biting task was used to assess precision biting. No significant differences were found in the hold forces, split forces or durations of split within or between the pain and pain-free conditions. Study II was a randomised, double-blinded study that included healthy participants of both sexes. A standardised chewing protocol of either 40- or 60-min of chewing was used, with a wash-out period. Subjective fatigue, pain characteristics, and functional measures were all assessed. Significant high subjective fatigue scores were induced in both the 40- and 60-min chewing trials. Significant but mild pain was induced only in the 60-min trial, and only in men. The induced pain area was significantly larger in the 60-min trial. The induced fatigue lasted up to 20 minutes after the end of the chewing while the increase in pain intensity and pain area did not until the first 10-min follow-up. Study III involved a series of chewing tasks involving viscoelastic soft and hard candies as well as a two-coloured gum. Optical imaging and analysis were conducted, and both bite force and the characteristics of pain and fatigue were assessed. Patients with painful TMD chewed the soft candies into particles that were fewer in number and which had a larger minimum Feret’s diameter after standardised chewing as compared to healthy pain-free control individuals. Surprisingly, the two-coloured gum was less mixed in the control cases. However, there were significant differences between the patients and the healthy controls in terms of self-assessed masticatory ability, mainly driven by painrelated issues. There was also obvious agreement between the patients’ self-assessed masticatory ability and the efficiency of their masticatory function. Conclusion: The three studies that form this doctoral thesis suggest that jaw muscle pain does not affect precision biting in humans; however, TMD patients with chronic myalgia exhibit impaired masticatory performance, with less efficiency of food communition, than those in the pain-free healthy control group. However, the excessive chewing model needs further adjustments in order to mimic TMD-pain, especially in women

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully