Thesis(M. Med (Anaesthesiology)--University of Limpopo(Medunsa Campus), 2010. / The purpose of this study was to interrogate the clinical relevance and cost effectiveness of the routine preoperative chest X-rays at DGMH. It was conducted as a descriptive cross-sectional prospective study of radiographic films in the Radiology department. A review of patients’ files and chest X-rays performed during a 6-month period from January to June 2008. Data from 100 patients’ files were included in the analysis. The age of patients ranged from 45 to 84years, the median age was 57years. The majority of patients younger than 50years were female, while the majority of male patients were over 50years. From a total of 100 patients only 8%(8 patients) were deemed unfit and consequently postponed or cancelled for further investigation and optimization. The cost for performing one routine chest X-ray was estimated to be R393 manpower, time and film inclusive, the total costs for the 100 patients included in this study being R39300. This study has provided some evidence that the routine preoperative chest X-rays can help in uncovering some abnormalities that were not apparent on clinical examination, it has pointed out that the impact of these uncovered abnormalities is very minimal on the planned surgery and that the costs associated with doing routine pre-operative chest X-rays can be substantial.

This dissertation was inspired by difficult decisions patients of chronic diseases have to make about about treatment options in light of uncertainty. We look at rheumatoid arthritis (RA), a chronic, autoimmune disease that primarily affects the synovial joints of the hands and causes pain and deformities. In this work, we focus on several parts of a computer-based decision tool that patients can interact with using gestures, ask questions about the disease, and visualize possible futures. We propose a hand gesture based interaction method that is easily setup in a doctor's office and can be trained using a custom set of gestures that are least painful. Our system is versatile and can be used for operations like simple selections to navigating a 3D world. We propose a point distribution model (PDM) that is capable of modeling hand deformities that occur due to RA and a generalized fitting method for use on radiographs of hands. Using our shape model, we show novel visualization of disease progression. Using expertly staged radiographs, we propose a novel distance metric learning and embedding technique that can be used to automatically stage an unlabeled radiograph. Given a large set of expertly labeled radiographs, our data-driven approach can be used to extract different modes of deformation specific to a disease.