No description available.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: The shape of the knee’s trochlear groove is a very important factor in the overall stability of the knee. However, a quantitative description of the normal three-dimensional geometry of the trochlea is not available in the literature. This is also reflected in the poor outcomes of patellofemoral arthroplasty (PFA). In this study, a standardised method for femoral parameter measurements on three-dimensional femur models was established. Using software tools, virtual femur models were aligned with the mechanical and the posterior condylar planes and this framework was used to measure the femoral parameters in a repeatable way. An artificial neural network (ANN), incorporating the femoral parameter measurements and classifications done by experienced surgeons, was used to classify knees into normal and abnormal categories. As a result, 15 knees in the database were classified by the ANN as being normal. Furthermore, the geometry of the normal knees was analysed by fitting B-spline curves and circular arcs on their sagittal surface curves to prove and reconfirm that the groove has a circular shape on a sagittal plane. Self-organising maps (SOM), which is a type of ANN, was trained with the acquired data of the normal knees and in this way the normal trochlear geometry could be predicted. The prediction of the anterior-posterior (AP) distance and the trochlear heights showed an average agreement of 97 % between the actual and the predicted normal geometries. A case study was conducted on four types of trochlear dysplasia to determine a normal geometry for these knees, and a virtual surface reconstruction was performed on them. The study showed that the trochlea was deepened after the surface reconstruction, having an average trochlea depth of 5.5 mm compared to the original average value of 2.9 mm. In summary, this research proposed a quantitative method for describing and predicting the normal geometry of a knee by making use of ANN and the femoral parameters that are unaffected by trochlear dysplasia. / AFRIKAANSE OPSOMMING: Die vorm van die trogleêre keep is ’n belangrike faktor in patella-stabiliteit. Tog is ’n kwantitatiewe beskrywing van die normale driedimensionele geometrie van die troglea nog nie beskikbaar nie, wat duidelik blyk uit die swak uitkomste van patellofemorale artroplastie (PFA). In hierdie studie is ’n gestandaardiseerde metode vir die meting van femorale parameters op grond van driedimensionele femurmodelle ontwikkel. Die femurmodel is in lyn gebring met die meganiese en posterior kondilêre vlak, welke raamwerk gebruik is om die femorale parameters op ’n herhaalbare wyse te meet. Die normale knieë is geklassifiseer met ’n kunsmatige neurale netwerk (ANN), wat die femorale parameter-mate sowel as die chirurgiese klassifikasie ingesluit het, en 15 knieë is gevolglik as normaal aangewys. Die normaleknie-geometrie is ontleed deur B-latkrommes en sirkelboë op die sagittale oppervlak-kurwes aan te bring om te bewys en te herbevestig dat die keep uit ’n sirkelvorm op ’n sagittale vlak bestaan. Die ingesamelde data van die normale knieë is ingevoer by selfreëlende kaarte (SOM), synde ’n soort ANN, wat die navorser in staat gestel het om die normale trogleêre geometrie te voorspel. Die voorspelling van die anterior-posterior (AP) afstand en die trogleêre hoogtes toon ’n gemiddelde ooreenkoms van meer as 97 % tussen die werklike en voorspelde normale geometrie. ’n Gevallestudie is op vier soorte trogleêre displasie uitgevoer om die normale geometrie te voorspel en ’n oppervlakrekonstruksie daarop uit te voer. Hierdie studie het getoon dat die troglea ná oppervlakrekonstruksie verdiep was, met ’n gemiddelde trogleadiepte van 5.5 mm in vergelyking met die aanvanklike gemiddelde waarde van 2.9 mm. Hierdie navorsing het dus ’n metode aan die hand gedoen vir die kwantitatiewe beskrywing en voorspelling van normale geometrie met behulp van ANN sowel as met die femorale parameters wat nie deur die trogleêre displasie geraak word nie.

No description available.