CBCT-based virtual surgical planning for predicting risk factors and preventive strategies of buccal plate perforation in maxillary premolar implant placement.

Abstract

BACKGROUND: Buccal plate perforation (BPP) is a common intraoperative complication during implant placement in the maxillary premolar region, compromising implant stability and long-term success. This study aimed to identify CBCT-based risk factors for BPP and develop a predictive model to optimize implant planning. MATERIALS AND METHODS: A retrospective analysis of cone beam computed tomography (CBCT) scans from 252 patients with missing maxillary premolars was performed. Virtual implant simulation evaluated the effects of alveolar bone type, alveolar bone inclination angle (ABIA), tooth position, and implant-to-bone angulation (IBA) on BPP incidence. Significant variables identified by Chi-squared and t-tests were further analyzed using multivariate logistic regression and receiver operating characteristic (ROC) analysis to determine predictive thresholds. RESULTS: Sex, tooth position, alveolar bone type, ABIA, and IBA were significantly associated with BPP (P < 0.05). Female patients, first premolar sites, and buccally projected-concave bone types showed higher risk. BPP risk increased when ABIA ≥ 24.94° or IBA ≥ 20.71°, whereas a 1 mm palatal shift from the alveolar ridge center reduced its incidence from 34.69% to 7.1% with minimal palatal perforation (2.6%). CONCLUSIONS: CBCT-based virtual planning enables individualized risk assessment and optimization of implant trajectory. This study identifies key quantitative thresholds for ABIA and IBA, which, when combined with minor adjustments in implant positioning, provide a robust strategy for improving surgical outcomes. Although these findings are derived from virtual simulations, clinical validation is required to confirm their applicability in real-world clinical settings.