BACKGROUND: The use of three-dimensional imaging has shown to provide advantages to the clinician in assessing bone morphology. The aim of this study will be to compare the diagnostic efficacy of cone beam computed tomography (CBCT) versus diagnostic clinical measurements in patients presenting with furcation involved maxillary first molars. METHODS: The study population included 20 patients with 34 maxillary first molar teeth with furcation involvement. Clinical horizontal and vertical probing measurements were compared to CBCT measurements taken by two calibrated examiners. RESULTS: Horizontal measurements showed a significant difference between Glickman class II and class III. There were no statistical significant differences with the horizontal measurements between clinical probing, bone sounding and CBCT measurements. CBCT vertical measurements were statistically greater than clinical probing measurements. CONCLUSION: The CBCT can provide similar horizontal measurements to standard clinical horizontal probing measurements and will provide a greater vertical dimension of a furcation defect to standard vertical probing measurements.

The use of computational modeling is an effective and inexpensive way to predict the response of complex systems to various perturbations. However, not until the early 1990s had this technology been used to predict the behavior of physiological systems, specifically the human skeletal system. To that end, a computational model of the human elbow joint was developed using computed topography (CT) scans of cadaveric donor tissue, as well as the commercially available software package SolidWorks™. The kinematic function of the joint model was then defined through 3D reconstructions of the osteoarticular surfaces and various soft-tissue constraints. The model was validated against cadaveric experiments performed by Hull et al and Fern et al that measured the significance of coronoid process fractures, lateral ulnar collateral ligament ruptures, and radial head resection in elbow joint resistance to varus displacement of the forearm. Kinematic simulations showed that the computational model was able to mimic the physiological movements of the joint throughout various ranges of motion including flexion/extension and pronation/supination. Quantitatively, the model was able to accurately reproduce the trends, as well as the magnitudes, of varus resistance observed in the cadaveric specimens. Additionally, magnitudes of ligament tension and joint contact force predicted by the model were able to further elucidate the complex soft-tissue and osseous contributions to varus elbow stability.