Extraction of biomedical indicators from gait videos

Gait has been an extensively investigated topic in recent years. Through the analysis of gait it is possible to detect pathologies, which makes this analysis very important to assess anomalies and, consequently, help in the diagnosis and rehabilitation of patients. There are some systems for analyzing gait, but they are usually either systems with subjective evaluations or systems used in specialized laboratories with complex equipment, which makes them very expensive and inaccessible. However, there has been a significant effort of making available simpler and more accurate systems for gait analysis and classification. This dissertation reviews recent gait analysis and classification systems, presents a new database with videos of 21 subjects, simulating 4 different pathologies as well as normal gait, and also presents a web application that allows the user to remotely access an automatic classification system and thus obtain the expected classification and heatmaps for the given input. The classification system is based on the use of gait representation images such as the Gait Energy Image (GEI) and the Skeleton Gait Energy Image (SEI), which are used as input to a VGG-19 Convolutional Neural Network (CNN) that is used to perform classification. This classification system is a vision-based system. To sum up, the developed web application aims to show the usefulness of the classification system, making it possible for anyone to access it.A marcha tem sido um tema muito investigado nos últimos anos. Através da análise da marcha é possível detetar patologias, o que torna esta análise muito importante para avaliar anómalias e consequentemente, ajudar no diagnóstico e na reabilitação dos pacientes. Existem alguns sistemas para analisar a marcha, mas habitualmente, ou estão sujeitos a uma interpretação subjetiva, ou são sistemas usados em laboratórios especializados com equipamento complexo, o que os torna muito dispendiosos e inacessíveis. No entanto, tem havido um esforço significativo com o objectivo de disponibilizar sistemas mais simples e mais precisos para análise e classificação da marcha. Esta dissertação revê os sistemas de análise e classificação da marcha desenvolvidos recentemente, apresenta uma nova base de dados com vídeos de 21 sujeitos, a simular 4 patologias diferentes bem como marcha normal, e apresenta também uma aplicação web que permite ao utilizador aceder remotamente a um sistema automático de classificação e assim, obter a classificação prevista e mapas de características respectivos de acordo com a entrada dada. O sistema de classificação baseia-se no uso de imagens de representação da marcha como a "Gait Energy Image" (GEI) e "Skeleton Gait Energy Image" (SEI), que são usadas como entrada numa rede neuronal convolucional VGG-19 que é usada para realizar a classificação. Este sistema de classificação corresponde a um sistema baseado na visão. Em suma, a aplicação web desenvolvida tem como finalidade mostrar a utilidade do sistema de classificação, tornando possível o acesso a qualquer pessoa

Particularities of visualisation of medical and wellness data through a digital patient avatar

In this work particularities of visualisation of medical and wellness data through a digital patient avatar are given from a standpoint of a proposed approach, under which data for a visualisation may be obtained from a variety of sources through defined interfaces, while end-user interfaces of distinct complexity and level of immersion into the model may be exposed to different categories of users. A short introduction of important medical data exchange standards, specifications and models is offered. A brief overview of projects relevant to a subject of this work is given. The proposed approach is presented along with examples of use-cases

Designing a Simulation showcasing the Pharmacological Effects of Beta-2-Agonists in Asthma Treatment; Virtual Reality as a supplement to traditional teaching methods

As educational technology evolves, there is a growing interest in applying VR in teaching complex scientific concepts that benefit from a visual and immersive learning environment. Motivated by the promising results of VR in medical education across multiple disciplines, we aimed to investigate the applicability and effectiveness of this technology in pharmacology education. This discipline, which involves understanding how drugs work within the human body, is often considered complex and challenging for students. However, it is a critical component of medical education and is essential in treating and preventing various diseases. The study was driven by two research inquiries. The primary inquiry aimed to explore the potential design possibilities of a virtual reality (VR) simulation for visualizing the pharmacological effects of beta-2-agonists in asthma treatment. The secondary question focused on evaluating the perspectives of students and educators regarding the efficacy of the VR application in learning pharmacology concepts compared to conventional teaching approaches. The application underwent two rounds of evaluation sessions with both students and teachers. Participants responded positively to the immersive learning experience, particularly appreciating the detailed visualizations and interactivity offered by the VR application. Their feedback highlighted the potential of VR to create a more intuitive understanding of complex pharmacological processes. Despite the evaluation phase featuring a limited number of participants, the received feedback suggested a promising potential for VR as an additional tool. The study, therefore, serves as a proof of concept, showcasing the possibilities of VR in enhancing pharmacology education and paving the way for future research and development in this area.Masteroppgave i Programvareutvikling samarbeid med HVLPROG399MAMN-PRO

Pressure drop and recovery in cases of cardiovascular disease: a computational study

The presence of disease in the cardiovascular system results in changes in flow and pressure patterns. Increased resistance to the flow observed in cases of aortic valve and coronary artery disease can have as a consequence abnormally high pressure gradients, which may lead to overexertion of the heart muscle, limited tissue perfusion and tissue damage. In the past, computational fluid dynamics (CFD) methods have been used coupled with medical imaging data to study haemodynamics, and it has been shown that CFD has great potential as a way to study patient-specific cases of cardiovascular disease in vivo, non-invasively, in great detail and at low cost. CFD can be particularly useful in evaluating the effectiveness of new diagnostic and treatment techniques, especially at early ‘concept’ stages. The main aim of this thesis is to use CFD to investigate the relationship between pressure and flow in cases of disease in the coronary arteries and the aortic valve, with the purpose of helping improve diagnosis and treatment, respectively. A transitional flow CFD model is used to investigate the phenomenon of pressure recovery in idealised models of aortic valve stenosis. Energy lost as turbulence in the wake of a diseased valve hinders pressure recovery, which occurs naturally when no energy losses are observed. A “concept” study testing the potential of a device that could maximise pressure recovery to reduce the pressure load on the heart muscle was conducted. The results indicate that, under certain conditions, such a device could prove useful. Fully patient-specific CFD studies of the coronary arteries are fewer than studies in larger vessels, mostly due to past limitations in the imaging and velocity data quality. A new method to reconstruct coronary anatomy from optical coherence tomography (OCT) data is presented in the thesis. The resulting models were combined with invasively acquired pressure and flow velocity data in transient CFD simulations, in order to test the ability of CFD to match the invasively measured pressure drop. A positive correlation and no bias were found between the calculated and measured results. The use of lower resolution reconstruction methods resulted in no correlation between the calculated and measured results, highlighting the importance of anatomical accuracy in the effectiveness of the CFD model. However, it was considered imperative that the limitations of CFD in predicting pressure gradients be further explored. It was found that the CFD-derived pressure drop is sensitive to changes in the volumetric flow rate, while bench-top experiments showed that the estimation of volumetric flow rate from invasively measured velocity data is subject to errors and uncertainties that may have a random effect on the CFD pressure result. This study demonstrated that the relationship between geometry, pressure and flow can be used to evaluate new diagnostic and treatment methods. In the case of aortic stenosis, further experimental work is required to turn the concept of a pressure recovery device into a potential clinical tool. In the coronary study it was shown that, though CFD has great power as a study tool, its limitations, especially those pertaining to the volumetric flow rate boundary condition, must be further studied and become fully understood before CFD can be reliably used to aid diagnosis in clinical practice.Open Acces

Cerebral Circulation

Diagnostics and diseases related to the cerebrovascular system are constantly evolving and updating. 3D augmented reality or quantification of cerebral perfusion are becoming important diagnostic tools in daily practice and the role of the cerebral venous system is being constantly revised considering new theories such as that of “the glymphatic system.” This book provides updates on models, diagnosis, and treatment of diseases of the cerebrovascular system

The role of mobile AR in facilitating nursing independent learning : the student experience

Augmented Reality (AR) is a new technology that creates virtual extra layers on a physical object. It allows bringing digital information into the real environment by blending those two worlds. Interacting with digital content is a unique AR feature that offers new learning opportunities integrated with mobile applications. Smartphone devices can be utilised as AR tools to support interactive learning. Previous research has reported multiple benefits of using mobile AR as a learning tool, including enhancing content understanding, improving long-term memory retention, and increasing student motivation. There are several potentials for utilising AR in the context of nursing education, such as promoting independent learning and facilitating a student-centred learning approach. Comparing to traditional education, the increased use of blended learning in healthcare and nursing education requires students to take more responsibility for their learning. Thus students' independent learning skills have become increasingly important. However, there is a lack of studies focusing on AR technology and independent learning. This research addresses this gap, aiming to investigate the feasibility of utilising AR technology to facilitate independent learning of nursing students while acquiring clinical skills. In this research, a design science research methodology was adopted, consisting of three phases. The first phase – problem identification – reviewed the literature and undertook an exploratory study. The literature revealed that the ability of AR to allow students to be immersed in a realistic experience has attracted educators to use this creative way of learning. Indicating the ability of AR to replace traditional teaching methods, it does this by encouraging self-directed learning between students and supporting student-centred learning (SCL). In SCL, the teacher’s role is as a facilitator who will enable students to learn independently and individually, while the learners are more responsible for their education. AR helps students to control their learning at their own pace and location. Moreover, the exploratory study investigated the current learning approach in terms of supporting independent learning, and 108 nursing students from The University of Salford answered an online questionnaire about their current learning. The results showed that the current learning environment is less supportive of independent learning due to many environmental obstacles, such as lack of feedback, accessibility issues, and lack of realism of manikins in clinical labs. The second phase – solution design – proposed a novel learning AR platform called Nursing Mobile Augmented Reality (NMAR), and the learning activities were designed based on self-regulated learning theory to overcome the current approach limitations. Learning with NMAR introduces a new learning strategy, aiming to enhance students’ independent learning by adding 16 interactive self-assessment. Utilising NMAR allows students the freedom to discover the solution independently and activate their learning. Lastly, the third phase – evaluation – included an experimental lab study, where the NMAR learning environment was deployed to create an overall positive user experience which might motivate the students to be independent learners. A novel NMAR platform to support nursing students was used individually by 34 students at The University of Salford, and both quantitative and qualitative data were collected from the students via questionnaires, screen recordings and semi-structured interviews. The quantitative data were looked at the NMAR aspects, while the qualitative data explored the users’ thoughts and perceptions about their experiences with NMAR. Evaluating the platform, understanding the users’ perception and comparing the current and proposed learning approaches together provide a deeper insight into the comprehensive user experience. The quantitative findings showed statistically significant differences in perceived usefulness scores between the current and NMAR approaches. The qualitative findings confirmed that the NMAR platform facilitates the self-regulated learning process greater extent than does the current approach. Accordingly, the findings of this research revealed that the proposed NMAR learning approach positively enhanced the students’ learning experience while acquiring clinical skills. The results confirmed that AR has a positive role in facilitating independent learning. Thus the research findings are expected to help nursing educators and policymakers to understand the feasibility of adopting AR technology in facilitating independent learning to support nursing education inside and outside the classroom. Furthermore, academia can use the proposed NMAR learning approach as relevant groundwork to initiate other related studies, which might help to fill the gap in the AR learning area

Development of advanced 3D medical analysis tools for clinical training, diagnosis and treatment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The objective of this PhD research was the development of novel 3D interactive medical platforms for medical image analysis, simulation and visualisation, with a focus on oncology images to support clinicians in managing the increasing amount of data provided by several medical image modalities. DoctorEye and Automatic Tumour Detector platforms were developed through constant interaction and feedback from expert clinicians, integrating a number of innovations in algorithms and methods, concerning image handling, segmentation, annotation, visualisation and plug-in technologies. DoctorEye is already being used in a related tumour modelling EC project (ContraCancrum) and offers several robust algorithms and tools for fast annotation, 3D visualisation and measurements to assist the clinician in better understanding the pathology of the brain area and define the treatment. It is free to use upon request and offers a user friendly environment for clinicians as it simplifies the implementation of complex algorithms and methods. It integrates a sophisticated, simple-to-use plug-in technology allowing researchers to add algorithms and methods (e.g. tumour growth and simulation algorithms for improving therapy planning) and interactively check the results. Apart from diagnostic and research purposes, it supports clinical training as it allows an expert clinician to evaluate a clinical delineation by different clinical users. The Automatic Tumour Detector focuses on abdominal images, which are more complex than those of the brain. It supports full automatic 3D detection of kidney pathology in real-time as well as 3D advanced visualisation and measurements. This is achieved through an innovative method implementing Templates. They contain rules and parameters for the Automatic Recognition Framework defined interactively by engineers based on clinicians’ 3D Golden Standard models. The Templates enable the automatic detection of kidneys and their possible abnormalities (tumours, stones and cysts). The system also supports the transmission of these Templates to another expert for a second opinion. Future versions of the proposed platforms could integrate even more sophisticated algorithms and tools and offer fully computer-aided identification of a variety of other organs and their dysfunctions

Experimental and theoretical analyses of compression induced muscle damage : aetiological factors in pressure ulcers

Pressure ulcers form a major problem in health care. They often occur when patients are bedridden, wheelchair bound or wearing prostheses. The ulcers can be very painful for the patient and often lead to prolonged hospitalization. In addition, the huge costs involved with treatment and prevention put a heavy burden on heath care budgets. Pressure ulcers occur often: between 14% and 33% of the patients in health care institutions develop an ulcer, ranging from discolouration of the skin to severe wounds involving necrosis of epidermis, extending to underlying bone, tendon and joints. It is clear that pressure ulcers are caused by prolonged mechanical loading, applied at the interface between skin and support surfaces. However, the aetiology of pressure ulcers is poorly understood. This forms an important obstacle in decreasing the unacceptably high prevalence figures. It is anticipated that a better understanding of the mechanobiological pathways leading to cell and tissue damage can lead to a breakthrough in reducing pressure ulcer prevalence. In addition, a solid scientific base may establish tools for objective risk assessment and judgement of preventive measures. The present study focuses on deep ulcers that initiate in skeletal muscle tissue, since deep ulcers are more extensive and often difficult to prevent. To obtain insight into the aetiology of these deep ulcers, it is necessary to understand the transfer from externally applied loads at the skin, to the local conditions that the cells experience within the tissue. In addition, the question which local conditions are harmful to the cell needs to be investigated. By combining knowledge on "what a cell feels" with knowledge on potentially harmful conditions, a better judgement of dangerous situations may be achieved. Although several causes of cell damage may play a role in the initiation of pressure ulcers, the present study focussed on the impact of cell deformations. To investigate the hypothesis that prolonged cell deformations lead to cell damage at clinically relevant strains, an experimental model system was developed. A key requirement of this experimental model is the possibility to study the role of cell deformation on cell damage independently of other possible causes of damage. To achieve this, in-vitro engineered muscle tissue constructs were developed. These constructs were compressed using a newly developed compression device. A custom made incubator system was developed to allow monitoring of the constructs for extended periods of time. In addition, a novel assay was developed to determine the viability of the cells during compression. This assay provides quantitative and spatial information on cell damage throughout a construct in a non-invasive manner, making use of fluorescent dyes which are visualized by confocal microscopy. The compression of the engineered muscle tissue constructs indicated that a significant increase in cell death occurs within 1-2 hours and that higher strain levels led to an earlier increase in damage. In addition, it was demonstrated that cell damage was uniformly distributed across the indented area of the construct, without a gradient in percentage dead cells between the centre and periphery of the constructs. The results strongly suggest that prolonged cell deformation was the predominant cause of cell damage in these experiments. This puts a new light on observations in literature which suggested that ischaemia is not the sole determinant for the onset of pressure ulcers. Nevertheless, more experiments are needed to clarify the role of prolonged cell deformations on cell damage. First, it is recommended that the actual local cell deformations are quantified during compression of the constructs. Furthermore, from the present experiments it could not be excluded that the compression of the constructs decreased the permeability of the construct and hence affected cellular metabolism. In future, measuring diffusion pathways of both small molecules and larger vital molecules, may indicate whether this change in permeability is significant. A numerical model was developed to predict local cell deformations, in response to tissue compression. Since the local cell deformations cannot be a-priori determined on the basis of homogenized tissue deformations, a multilevel finite element approach was adopted. In this approach, cell deformations are predicted from detailed nonlinear finite element analyses of the local microstructures of the tissue, which consist of an arrangement of cells embedded in a matrix material. To avoid unacceptably large computational times, the multilevel model was designed to run on a parallel computer system. Application of the multilevel model showed that the heterogeneity of the microstructure of the tissue has a profound impact on local cell deformations, which highly exceeded macroscopic tissue deformations. Moreover, microstructural heterogeneity led to complex cell shapes and caused non-uniform deformations within the cells. To investigate the evolution of compression induced damage in skeletal muscle tissue, the multilevel model was extended with a damage law, which was derived from the in-vitro experiments. With this model, the compression of muscle tissue against a bony prominence was simulated. The percentage of cell damage in the microstructure of the tissue was computed, which could be extrapolated to the bulk tissue level. In the present form, a schematic geometry was considered that intended to elucidate general patterns of tissue damage evolution. The simulations confirmed that it is not feasible to predict the onset of tissue damage on the basis of externally applied loading conditions at the skin surface alone, since these externally applied loads are not indicative of the local mechanical conditions that the cells experience within the tissue. In addition, the simulations showed that it is necessary to consider the local load history of the cells, and the tolerance of the tissue. These findings may explain why a strikingly large variability in load/time threshold values was found in animal studies, which attempted to relate external mechanical to tissue damage, thereby ignoring the local mechanical conditions within the tissue. At present, it is premature to utilize the models presented in this thesis in clinical practice, since the extrapolation towards human patients requires more research. Clearly, further extensions and validation of the numerical model with experimental animal models will be required. This should finally lead to the application in more realistic cases, involving patient data on geometry and tissue properties. Nevertheless, the present models provided an essential step towards evidence based risk assessment and prevention