Osteoarthritis (OA) is one of the most common pathologies encountered in dry bone contexts. However, even with the wealth of publications on documenting the presence of OA from skeletons, these studies prove to be largely incomparable due to different scoring methodologies and procedures in calculating prevalence. The standardization of a new OA data collection procedure would mitigate variability in evaluating, scoring, and calculating the prevalence of OA, thus allowing accurate comparison between studies. However, this level of data collection has often been described as unwieldy and lacking concordance. This research outlines a new methodology that utilizes Geographic Information Systems (GIS) to record OA characteristics, levels of expression, and spatial arrangement on the articular surfaces of the arm. The data was then processed using the analysis and visual rendering capabilities of GIS providing examples of OA patterning on the articular surface, within the joint, and within the individual. Using this method, large standardized OA datasets can be stored and the patterns within them modeled through the use of digitization, composite raster overlays, and modified binning techniques. The patterns recorded by this analysis can offer a more robust dataset on OA occurring within the arm that can provide the ability to explore OA progression and its relationship with biomechanical factors in larger datasets.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7776 |
| Date | 01 January 2019 |
| Creators | Biernaski, Adam |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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