Prediction of excess cement residues using a regression model to avoid peri-implant diseases: An in vitro study

Abstract

Dental implant restorations attached to cement can potentially result in peri-implant mucositis and peri-implantitis if cement residues are present. Effectively predicting and eliminating such dental cement residues is crucial for preventing complications. This study focuses on creating a regression model using the pixel values to predict the Excess Cement Residues (ECR) by employing an octagonal surface imaging approach. A model featuring gingival imitation, ten abutments, and ten crowns was created, and the cemented implants underwent thorough photographic and analytical assessment. The ECR was determined through two distinct approaches: the Computerized Planimetric Method (CPM) and the weighing method. Across ten implants in this in vitro study, ECR varied from 0.3 to 21 mg, with an average of 5.69 mg. The findings reveal a higher amount of ECR on the distal, mesiobuccal, and mesial sides. Utilizing Pearson’s correlation, a coefficient value of r = 0.786 signifies a strong correlation between CPM and the weighing method. The regression model further aids in predicting ECR based on pixel values. The octagonal surface imaging approach not only vividly captures information about ECR in the implant cementation region but also emphasizes the feasibility of ImageJ as an effective tool for detecting ECR. The congruence between CPM and the weighing method results supports the application of the regression model for precise ECR prediction.