PubMed · 14920725
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I FAZZARI. 1951. [Stroma].. https://pubmed.ncbi.nlm.nih.gov/14920725/
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OBJECTIVES: To identify histological features that distinguish amalgam-associated oral lichenoid reactions (AAOLR) from oral lichen planus (OLP). METHODS: Oral pathologists provided their opinion as to the possibility of distinguishing AAOLR and OLP histologically, the features important in distinguishing AAOLR from OLP and the diagnosis of 12 AAOLR and 12 OLP cases including the features that drew them to their conclusion. RESULTS: There was considerable variation between pathologists in their ability to distinguish the AAOLR and OLP cases. The sensitivity and specificity for histological diagnosis were 40% and 32% respectively. There were four features that were used most commonly to discriminate between AAOLR and OLP: an inflammatory infiltrate located deep to superficial infiltrate in some or all areas; a focal perivascular infiltrate; plasma cells in the connective tissue and neutrophils in the connective tissue. Each was independently predictive of AAOLR or OLP (P < 0.028). CONCLUSIONS: This study confirms the uncertainty of the diagnostic histological differences between AAOLR and OLP. Distinguishing these conditions should not rely on histology alone, but should be based on a synthesis of all available information including history, examination, histopathology and skin patch testing.
Tool-tissue interaction is most accurately modeled as an imposed displacement constraint, thus augmenting the traditional finite element equation, Ku=f, to a 2 x 2 block system. This augmentation does two things: it enlarges the system, and it introduces the Schur complement(S) during the solution. This research has focused on efficient methods to update the Schur complement and it's inverse during displacement constraint removal and addition to allow for soft-tissue cutting to occur in real-time. By taking advantage of how the constraints impact the Schur complement, removal and addition of these constraints are handled by either performing a rank-2 or rank-1 update on S(-1), respectively. This greatly reduces the computational load on the CPU; and through use of timing tests shows that the update frequencies are well within an acceptable range for tactile feedback. To further solidify this method as being highly useful, a prototypical simulator has been developed that allows users to haptically perform bi-manual soft-tissue cutting.
Enhanced visualization of an operating scene presented by a robotically assisted tele-manipulator system such as the da Vinci(TM) can be provided through the use of augmented reality facilities. Generating overlays from 3D models and the intra-operative video allows the surgeon to acquire greater information about the surgical scene. Tool-tissue interactions must be tracked to ensure the overlays are updated regularly and accurately. The work presented here describes how these interactions may be modelled by integrating a nonlinear finite element model with the 3D reconstruction and using the tool kinematic data as input to the deformation.