In Journal of clinical periodontology ; h5-index 58.0
AIM : The rate of physiological bone remodeling (PBR) occurring after implant placement has been associated with the later onset of progressive bone loss and peri-implantitis leading to medium- and long-term implant therapy failure. It is still questionable, however, whether PBR is associated with specific bone characteristics. The aim of this study was to assess if radiomic analysis could reveal not readily appreciable bone features useful for the prediction of PBR.
MATERIALS AND METHODS : Radiomic features were extracted from the radiographs performed at implant placement (T0) using LifeX software and, due to the multi-center design of the source study, ComBat harmonization was applied to the cohort. Different machine-learning models were trained on selected radiomic features to develop and internally validate algorithms able to predict high PBR. In addition, results of the algorithm were included in a multivariate analysis with other clinical variables (tissue thickness and depth of implant position) to test its independent correlation with PBR.
RESULTS : Specific radiomic features extracted at T0 are associated with higher physiological bone remodeling around tissue-level implants after 3 months of unsubmerged healing (T1). In addition, taking advantage of machine-learning methods, a Naive Bayes model was trained using radiomic features selected by Fast Correlation Based Filter (FCBF) showed the best performance in the prediction of PBR (AUC = 0.751, sensitivity = 66.0%, specificity = 68.4%, positive predictive value = 73.3%, negative predictive value = 60.5%). In addition, results of the whole-model were included in a multivariate analysis with tissue thickness and depth of implant position and still resulted to be independently associated with PBR (p-value < 0.01).
CONCLUSION : The combination of radiomics and machine-learning methods seems to be a promising approach for the early prediction of PBR. Such an innovative approach could be also used for the study of not readily disclosed bone characteristics helping in explaining not fully understood clinical phenomena. Although promising, the performance of the radiomic model should be improved in terms of specificity and sensitivity by further studies on this field. This article is protected by copyright. All rights reserved.
Troiano Giuseppe, Fanelli Francesco, Rapani Antonio, Zotti Matteo, Lombardi Teresa, Zhurakivska Khrystyna, Stacchi Claudio
2023-Feb-26
Artificial Intelligence, Dental Implants, Dental Radiology, Early Marginal Bone Loss, Physiological Bone Remodeling, Radiomics
