Predictive Analysis for Success and Complications in Dental Implant Therapy using Artificial Intelligence Models
Introduction: Dental implant therapy is a predictable treatment for tooth replacement, but its success is influenced by multiple patient, surgical, and systemic factors. Traditional statistical models, such as logistic regression, are limited in capturing nonlinear relationships among these variables. Artificial intelligence (AI) can integrate diverse predictors to enhance clinical risk assessment and improve treatment outcomes. Objective: To identify key predictors of dental implant success and develop AI-based models capable of accurately forecasting implant outcomes using clinical and surgical parameters. Methods: This retrospective cohort study analyzed data from 172 patients (219 implants) placed between 2020 and 2021. Patient demographics, systemic health (smoking, diabetes), surgical variables, and implant characteristics were evaluated. Univariate and multivariate logistic regression identified independent predictors. Machine-learning models Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest were trained using stratified 10-fold cross-validation and SMOTE balancing. Performance was assessed via accuracy, precision, recall, F1-score, AUC, and Brier score; calibration and Decision-Curve Analysis (DCA) evaluated model reliability and clinical benefit. Results: Overall implant success was 91.3%, with smoking (AOR D 2.3; 95% CI 1.3–4.1; p D 0.001) and diabetes mellitus (AOR D 1.8; 95% CI 1.1–3.5; p D 0.03) emerging as independent predictors of failure. Flapless surgery demonstrated a protective effect (AOR D 0.7; 95% CI 0.5–0.9; p D 0.04). The Random Forest model achieved the highest predictive performance (Accuracy D 87.4%, AUC D 0.91) and showed good calibration (Brier D 0.07) with superior clinical net benefit on DCA. Conclusion: AI-based modeling offers a robust, data-driven approach for predicting dental implant success by integrating multifactorial clinical parameters. Incorporating such models into clinical workflows can enhance patient-specific risk assessment and improve long-term implant outcomes.