Jul 2026· International Arab Journal of Dentistry· Vol 17· 0 citations
TL;DR
AI-based modeling offers a robust, data-driven approach for predicting dental implant success by integrating multifactorial clinical parameters and incorporating such models into clinical workflows can enhance patient-specific risk assessment and improve long-term implant outcomes.
Abstract
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.
Predicting the long-term success of dental implants in medically compromised patients remains a major challenge in implant dentistry due to the influence of multiple systemic and local risk factors. This retrospective study analyzed data from 300 medically compromised patients to develop and evaluate artificial intelligence (AI)-based predictive models for implant outcomes using demographic, systemic and implant-related variables. Machine learning models, particularly the random forest algorithm, demonstrated higher predictive accuracy than conventional statistical methods. Systemic conditions, smoking status and bone quality were identified as significant predictors of implant success. Thus, data shows the AI-based predictive models can facilitate personalized risk assessment and enhance clinical decision-making in implant dentistry.
Accurate prediction of dental implant longevity remains difficult using readily available non-clinical patient factors alone. Therefore, it is of interest to evaluate the internally validated 5-year implant longevity models using demographic, systemic and behavioural and maintenance variables from 864 implants in 326 patients. Logistic Regression, Random Forest and Gradient Boosting Machine models were assessed using AUC, Brier score, calibration and decision-curve analysis. The 5-year failure rate was 18.2% and Gradient Boosting Machine showed the best performance with an AUC of 0.64 and Brier score of 0.179. Thus, data shows the non-clinical variables provide moderate predictive accuracy and may support early risk stratification and future multimodal prediction models.
Molecular markers emerged as the strongest predictors, whereas conventional clinical variables showed limited value, and the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery is highlighted.
L. Sydorchuk, R. Gumennyi, Miroslav Škoda et al.· Computation· 1 citation
RF-based models can accurately and equitably predict perioperative complications in diverse spine surgery contexts, supporting personalized counseling, targeted monitoring, and optimized resource allocation.
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Peri-implantitis causes progressive bone loss around dental implants and arises from interacting microbial, host, behavioral and implant factors. Conventional clinical and radiographic criteria capture this complexity only partially, whereas machine learning can integrate heterogeneous data across early detection, risk stratification and outcome prediction. Convolutional neural networks detect peri-implant bone loss on radiographs with high reported accuracy. Supervised models, such as random forests, combine clinical and patient-level data to estimate individual risk, often outperforming conventional statistics. Thus, data shows that evidence rests largely on small, single-center, retrospective datasets, so machine learning should augment rather than replace clinical judgment until standardized, externally validated models are available.
Artificial intelligence (AI) is increasingly being incorporated into digital dentistry. In implant dentistry, machine-learning and deep-learning systems have been investigated for radiographic diagnosis, anatomical segmentation, treatment planning, prosthetically driven implant positioning, and prediction of implant-related outcomes. To systematically evaluate the clinical applications of AI in implant dentistry, with particular emphasis on diagnostic accuracy, treatment planning, prognosis, external validation, generalizability, and clinical applicability. A systematic review will be conducted according to PRISMA 2020. PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library will be searched from inception to the final search date. Studies evaluating AI, machine learning, deep learning, neural networks, or related computational approaches in human implant dentistry will be considered. Diagnostic studies will be assessed using QUADAS-2, prognostic studies using QUAPAS where applicable, and prediction-model studies using PROBAST+AI. Reporting quality of prediction models will be assessed using TRIPOD+AI. Current evidence demonstrates promising AI performance in anatomical segmentation, CBCT analysis, implant treatment planning, prosthetically driven positioning, and prediction of implant-related outcomes. However, the literature is heterogeneous with respect to datasets, algorithms, reference standards, outcome definitions, validation procedures, and performance measures. Many studies remain retrospective, and external validation and evidence of clinical utility are comparatively limited. AI has considerable potential as a decision-support technology in implant dentistry. The principal research priority is now progression from technical accuracy toward externally validated, explainable, calibrated, clinically useful, and patient-centered AI systems. Keywords AI; machine learning; deep learning; dental implants; implant dentistry; CBCT; implant planning; implant prognosis; peri-implantitis; digital dentistry.
R. Raghavan, Shajahan P. A., A. R· International Journal of Res...· 0 citations
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