Résumé
Abstract Objective This study aimed to validate the integrated correlation between the buccal bone and gingival thickness of the anterior maxilla, and to gain insight into the reference plane selection when measuring these two tissues before treatment with implants. Methodology Cone beam computed tomography (CBCT) and model scans of 350 human subjects were registered in the coDiagnostiX software to obtain sagittal maxillary incisor sections. The buccal bone thickness was measured at the coronal (2, 4, and 6 mm apical to the cementoenamel junction [CEJ]) and apical (0, 2, and 4 mm coronal to the apex plane) regions. The buccal gingival thickness was measured at the supra-CEJ (0, 1mm coronal to the CEJ) and sub-CEJ regions (1, 2, 4, and 6 mm apical to the CEJ). Canonical correlation analysis was performed for intergroup correlation analysis and investigation of key parameters. Results The mean thicknesses of the buccal bone and gingiva at different levels were 0.64~1.88 mm and 0.66~1.37 mm, respectively. There was a strong intergroup canonical correlation between the thickness of the buccal bone and that of the gingiva (r=0.837). The thickness of the buccal bone and gingiva at 2 mm apical to the CEJ are the most important indices with the highest canonical correlation coefficient and loadings. The most and least prevalent subgroups were the thin bone and thick gingiva group (accounting for 47.6%) and the thick bone and thick gingiva group (accounting for 8.6%). Conclusion Within the limitations of this retrospective study, the thickness of the buccal bone is significantly correlated with that of the buccal gingiva, and the 2 mm region apical to the CEJ is a vital plane for quantifying the thickness of these two tissues
Résumé
@#At present, implant surgery robots have basically achieved "surgical intelligence", but "brain-inspired intelligence" of robots is still in the stage of theory and exploration. The formulation of a clinical implantation plan depends on the timing of implantation, implantation area, bone condition, surgical procedure, patient factors, etc., which need to evaluate the corresponding clinical decision indicators and clinical pathways. Inspired by evidence-based medicine and the potential of big data and deep learning, combined with the data characteristics of clinical decision indicators and clinical pathways that can be quantitatively or qualitatively analyzed, this review simulates the cognitive behavior and neural mechanisms of the human brain and proposes a feasible brain-inspired intelligence scheme by predicting the decision indices and executing clinical pathways intelligently, that is, "select clinical indicators and clarify clinical pathways -- construct database -- use deep learning to intelligently predict decision indicators -- intelligent execution of clinical pathways -- brain-inspired intelligence of implant decision-making". Combined with the previous research results of our team, this review also describes the process of realization of brain-inspired intelligence for immediate implant timing decisions, providing an example of the comprehensive realization of brain-inspired intelligence of implant surgery robots in the future. In the future, how to excavate and summarize other clinical decision factors and select the best way to realize the automatic prediction of evidence-based clinical indicators and pathways and finally realize the complete intellectualization of clinical diagnosis and treatment processes will be one of the directions that dental clinicians need to strive for.