Development of the Telemetrical Intraoperative Soft Tissue Tension Monitoring System in Total Knee Replacement with MEMS and ASIC Technologies

The alignment of the femoral and tibial components of the Total Knee Arthoplasty (TKA) is one of the most important factors to implant survivorship. Hence, numerous ligament balancing techniques and devices have been developed in order to accurately balance the knee intra-operatively. Spacer block, tensioner and tram adapter are instruments that allow surgeons to qualitatively balance the flexion and extension gaps during TKA. However, even with these instruments, the surgical procedure still relies on the skill and experience of the surgeon. The objective of this thesis is to develop a computerized surgical instrument that can acquire intra-operative data telemetrically for surgeons and engineers. Microcantilever is chosen to be used as the strain sensing elements. Even though many high end off-the-shelf data acquisition components and integrated circuit (IC) chips exist on the market, yet multiple components are required to process the entire array of microcantilevers and achieve the desired functions. Due to the size limitation of the off-chip components, an Application Specific Integrated Circuit (ASIC) chip is designed and fabricated. Using a spacer block as a base, sensors, a data acquisition system as well as the transmitter and antenna are embedded into it. The electronics are sealed with medical grade epoxy

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges

"El nen s'ha menjat una aranya": The development of narratives in Catalan speaking children

The production of a well-constructed narrative is the culmination of several years of language acquisition and is an important milestone in children's development. There is no current description of narrative development for Catalan speaking children. This study collected elicited narratives in Catalan from 118 children aged 4;0-10;11. Narratives were scored for macrostructure and microstructure. Narrative scores improved with age with maximum performance for macrostructure by 9 years. Children's ability to use micro-structural components of Catalan is variable with some developments continuing beyond 9 years. The results are discussed in relation to theoretical arguments about universal and specific features of narrative development. We conclude by highlighting the usefulness of the new test for future language assessment of children acquiring Catalan

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Risk of asthma in children diagnosed with bronchiolitis during infancy: Protocol of a longitudinal cohort study linking emergency department-based clinical data to provincial health administrative databases

Introduction The Canadian Bronchiolitis Epinephrine Steroid Trial (CanBEST) and the Bronchiolitis Severity Cohort (BSC) study enrolled infants with bronchiolitis during the first year of life. The CanBEST trial suggested that treatment of infants with a combined therapy of high-dose corticosteroids and nebulised epinephrine reduced the risk of admission to hospital. Our study aims to - (1) quantify the risk of developing asthma by age 5 and 10 years in children treated with high-dose corticosteroid and epinephrine for bronchiolitis during infancy, (2) identify risk factors associated with development of asthma in children with bronchiolitis during infancy, (3) develop asthma prediction models for children diagnosed with bronchiolitis during infancy. Methods and analysis We propose a longitudinal cohort study in which we will link data from the CanBEST and BSC study with routinely collected data from provincial health administrative databases. Our outcome is asthma incidence measured using a validated health administrative data algorithm. Primary exposure will be treatment with a combined therapy of high-dose corticosteroids and nebulised epinephrine for bronchiolitis. Covariates will include type of viral pathogen, disease severity, medication use, maternal, prenatal, postnatal and demographic factors and variables related to health service utilisation for acute lower respiratory tract infection. The risk associated with development of asthma in children treated with high-dose corticosteroid and epinephrine for bronchiolitis will be assessed using multivariable Cox proportional hazards regression models. Prediction models will be developed using multivariable logistic regression analysis and internally validated using a bootstrap approach. Ethics and dissemination Our study has been approved by the ethics board of all four participating sites of the CanBEST and BSC study. Finding of the study will be disseminated to the academic community and relevant stakeholders through conferences and peer-reviewed publications. Trial registration number ISRCTN56745572; Post-results

Animal models of atherosclerosis.

Atherosclerosis is a significant cause of morbidity and mortality globally. Many animal models have been developed to study atherosclerosis, and permit experimental conditions, diet and environmental risk factors to be carefully controlled. Pathophysiological changes can be produced using genetic or pharmacological means to study the harmful consequences of different interventions. Experiments using such models have elucidated its molecular and pathophysiological mechanisms, and provided platforms for pharmacological development. Different models have their own advantages and disadvantages, and can be used to answer different research questions. In the present review article, different species of atherosclerosis models are outlined, with discussions on the practicality of their use for experimentation.GT was supported by a BBSRC Doctoral Training Award and thanks the Croucher Foundation of Hong Kong for the generous support of his clinical assistant professorship. YC is supported by the ESRC

Animal models of atherosclerosis.

Atherosclerosis is a significant cause of morbidity and mortality globally. Many animal models have been developed to study atherosclerosis, and permit experimental conditions, diet and environmental risk factors to be carefully controlled. Pathophysiological changes can be produced using genetic or pharmacological means to study the harmful consequences of different interventions. Experiments using such models have elucidated its molecular and pathophysiological mechanisms, and provided platforms for pharmacological development. Different models have their own advantages and disadvantages, and can be used to answer different research questions. In the present review article, different species of atherosclerosis models are outlined, with discussions on the practicality of their use for experimentation.GT was supported by a BBSRC Doctoral Training Award and thanks the Croucher Foundation of Hong Kong for the generous support of his clinical assistant professorship. YC is supported by the ESRC