Multivariate analysis of the influence of peri-implant clinical parameters and local factors on radiographic bone loss in the posterior maxilla: a retrospective study on 277 dental implants.

OBJECTIVES: The aim of the present study was to investigate whether peri-implant clinical parameters (modified plaque index (mPI), bleeding and/or suppuration on probing (B/SOP)) and local factors (type of prostheses, screw emergence, platform diameter, and abutment angulation) might contribute to the development of additional bone loss and peri-implantitis around dental implants. MATERIALS AND METHODS: Two hundred seventy-seven external hex connection implants placed in the posterior maxilla of 124 patients were retrospectively evaluated. They were divided into two groups: physiologic bone loss < 2 mm (PBL) or additional bone loss ≥ 2 mm (ABL). GEE logistic regression was applied to evaluate the influence of type of prostheses (implant-supported single crown (ISSC), fixed partial denture (ISFPD), and full denture (ISFD)) and clinical parameters (mPI and S/BOP) on bone loss. RESULTS: Among the 277 implants, 159 (57.4%) presented PBL and 118 (42.6%) presented ABL. Within the ABL group, 20.6% implants were diagnosed with peri-implantitis. mPI significantly correlated with the type of prosthesis and the highest value of mPI (index = 3) was observed in ISFD (23.8%). Moreover, peri-implantitis was more frequently associated with ISFD (32.79%) than ISSC and ISFDP (13.79% and 13.48, respectively) CONCLUSIONS: ISFD in the posterior maxilla presented high rates of ABL and showed a higher prevalence of peri-implantitis. None of the local factors seemed to contribute to the development of these conditions. Further investigations are needed to prospectively support the results of the present study. CLINICAL RELEVANCE: Patients rehabilitated with ISFD should be carefully monitored and have more frequent maintenance visits to prevent or control peri-implant bone loss.

On the Geodesic Distance in Shapes K-means Clustering.

In this paper, the problem of clustering rotationally invariant shapes is studied and a solution using Information Geometry tools is provided. Landmarks of a complex shape are defined as probability densities in a statistical manifold. Then, in the setting of shapes clustering through a K-means algorithm, the discriminative power of two different shapes distances are evaluated. The first, derived from Fisher-Rao metric, is related with the minimization of information in the Fisher sense and the other is derived from the Wasserstein distance which measures the minimal transportation cost. A modification of the K-means algorithm is also proposed which allows the variances to vary not only among the landmarks but also among the clusters.