A hybrid unsupervised clustering method for predicting the risk of dental implant loss

Abstract

ObjectivesThe aim of this study was to predict the risk of dental implant loss by clustering features associated with implant survival rates. Materials and methodsMultiple clinical features from 8513 patients who underwent single implant placement were retrospectively analysed. A hybrid method integrating unsupervised learning algorithms with survival analysis was employed for data mining. Two-step cluster, univariate Cox regression, and Kaplan‒Meier survival analyses were performed to identify the clustering features associated with implant survival rates. To predict the risk of dental implant loss, nomograms were constructed on the basis of time-stratified multivariate Cox regression. ResultsSix clusters with distinct features and prognoses were identified using two-step cluster analysis and Kaplan‒Meier survival analysis. Compared with the other clusters, only one cluster presented significantly lower implant survival rates, and six specific clustering features within this cluster were identified as high-risk factors, including age, smoking history, implant diameter, implant length, implant position, and surgical procedure. Nomograms were created to assess the impact of the six high-risk factors on implant loss for three periods: 1) 0–120 days, 2) 120–310 days, and 3) more than 310 days after implant placement. The concordance indices of the models were 0.642, 0.781, and 0.715, respectively. ConclusionsThe hybrid unsupervised clustering method, which clusters and identifies high-risk clinical features associated with implant loss without relying on predefined labels or target variables, represents an effective approach for developing a visual model for predicting implant prognosis. However, further validation with a multimodal, multicentre, prospective cohort is needed. Clinical significanceVisual prognosis prediction utilizing this nomogram that predicts the risk of implant loss on the basis of clustering features can assist dentists in preoperative assessments and clinical decision-making, potentially improving dental implant prognosis.