title: Prediction of individual implant bone levels and the existence of implant "phenotypes".
abstract: OBJECTIVE
  To cluster implants placed in patients of a private practice and identify possible implant "phenotypes" and predictors of individual implant mean bone levels (IIMBL).
  
  
  METHODS
  Clinical and radiographical variables were collected from 72 implant-treated patients with 237 implants and a mean 7.4 ± 3.5 years of function. We clustered implants using the k-means method guided by multidimensional unfolding. For predicting IIMBL, we used principal component analysis (PCA) as a variable reduction method for an ensemble selection (ES) and a support vector machines models (SVMs). Network analysis investigated variable interactions.
  
  
  RESULTS
  We identified a cluster of implants susceptible to peri-implantitis (96% of the implants in the cluster were affected by peri-implantitis) and two overlapping clusters of implants resistant to peri-implantitis. The cluster susceptible to peri-implantitis showed a mean IIMBL of 5.2 mm and included implants placed mainly in the lower front jaw and in mouths having a mean of eight teeth. PCA extracted the parameters such as number of teeth, full-mouth plaque scores, implant surface, periodontitis severity, age and diabetes as significant in explaining the data variability. ES and SVMs showed good results in predicting IIMBL (root-mean-squared error of 0.133 and 0.149, 10-fold cross-validation error of 0.147 and 0.150, respectively). Network analysis revealed limited interdependencies of variables among peri-implantitis-affected and non-affected implants and supported the hypothesis of the existence of distinct implant "phenotypes."
  
  
  CONCLUSIONS
  Two implant "phenotypes" were identified, one with susceptibility and another with resistance to peri-implantitis. Prediction of IIMBL could be achieved by using six variables.
authors.affiliation: Center for Research and Applications of Nonlinear Systems, Department of Mathematics, University of Patras, Patras, Greece.
authors.forename: Georgios
authors.initials: G
authors.lastname: Papantonopoulos
authors.affiliation: Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Greece.
authors.forename: Christos
authors.initials: C
authors.lastname: Gogos
authors.affiliation: Computer Systems Laboratory, Department of Electrical and Computer Engineering, University of Patras, Patras, Greece.
authors.forename: Efthymios
authors.initials: E
authors.lastname: Housos
authors.affiliation: Laboratory of Nonlinear Systems and Applied Analysis, Department of Mathematics, University of Patras, Patras, Greece.
authors.forename: Tassos
authors.initials: T
authors.lastname: Bountis
authors.affiliation: Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University, Amsterdam, The Netherlands.
authors.forename: Bruno G
authors.initials: BG
authors.lastname: Loos
mesh_terms: D015921:Dental Implants; D019094:Dental Prosthesis, Implant-Supported; D005260:Female; D006115:Greece; D006801:Humans; D008297:Male; D008875:Middle Aged; D057873:Peri-Implantitis / Q000175:diagnosis*; D010641:Phenotype; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D060388:Support Vector Machine
chemical_list: D015921:Dental Implants
keywords: alveolar bone; complex disease; computational biology; dental implant; network analysis; peri-implantitis; statistical learning theory