Distinguishing predictive profiles for patientbased risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis

Abstract

Objective. To investigate whether specific predictive profiles for patient-based risk assessment-diagnostics can be applied in different subtypes of peri-implantitis. Materials and methods. This study included patients with at least two implants (one or more presenting signs of peri-implantitis). Anamnestic, clinical, and implant-related parameters were collected and scored into a single database. Dental implant was chosen as the unit of analysis, and a complete screening protocol was established. The implants affected by peri-implantitis were then clustered into three subtypes in relation to the identified triggering factor; purely plaque-induced or prosthetically or surgically triggered peri-implantitis. Statistical analyses were performed to compare the characteristics and risk factors between peri-implantitis and healthy implants, as well as to compare clinical parameters and distribution of risk factors between plaque, prosthetically and surgically triggered peri-implantitis. The predictive profiles for subtypes of peri-implantitis were estimated using data mining tools including regression methods and C4.5 decision trees. Conclusions. It can be concluded that plaque induced and prosthetically and surgically triggered peri-implantitis are different entities associated with distinguishing predictive profiles, hence, the appropriate causal treatment approach remains necessary. The advanced data mining model developed in this study seems to be a promising tool for diagnostics of peri-implantitis subtypes.

Sandro Radovanović

Assistant Professor at University of Belgrade

My research interests include machine learning, development and design of decision support systems, decision theory, and fairness and justice concepts in algorithmic decision making.