Artificial Intelligence Prediction of Prosthetic Complications in Implant-Supported Restorations: A Pilot Study

Abstract

Background

Implant-supported restorations demonstrate high long-term survival; however, technical and biological complications such as screw loosening, ceramic chipping or fracture, loss of retention, framework fracture, and peri-implant inflammatory disease remain clinically relevant. Artificial intelligence (AI) and machine-learning methods may facilitate individualized risk estimation by integrating patient-, implant-, prosthetic-, occlusal-, radiographic-, and maintenance-related variables.

Objective

To develop and demonstrate a clinically oriented machine-learning framework for predicting prosthetic and prosthesis-associated biological complications in implant-supported restorations and to define methodological requirements for subsequent prospective validation.

Methods

A prospective prediction-model framework was designed around patient- and restoration-level variables, including age, smoking, diabetes, history of periodontitis, bruxism or parafunction, implant location, implant dimensions, implant angulation, prosthetic material, retention mode, cantilever extension, occlusal characteristics, peri-implant clinical and radiographic parameters, and maintenance adherence. Logistic regression, random forest, and gradient-boosting models were prespecified as candidate approaches. A synthetic dataset of 60 restorations was used solely to demonstrate the proposed analytical workflow and statistical presentation.

Results

The synthetic demonstration dataset contained 60 restorations, of which 18 (30.0%) were assigned a composite complication outcome within a simulated 12-month follow-up period. The simulated complication categories included screw loosening (n=8), ceramic chipping (n=5), loss of retention (n=4), peri-implant inflammation (n=3), screw fracture (n=2), and framework fracture (n=1); categories were permitted to overlap. In the demonstration analysis, simulated ROC-AUC values were 0.78 for logistic regression, 0.87 for random forest, and 0.84 for gradient boosting. These values are synthetic demonstration outputs and do not represent clinical model performance.

Conclusion

A machine-learning framework integrating patient, implant, prosthetic, occlusal, peri-implant, and maintenance variables may provide a structured approach for future prediction of complications associated with implant-supported restorations. However, the numerical findings presented here are simulation-based and cannot be interpreted as evidence of clinical predictive performance. A prospective, adequately powered, multicentre study with prespecified outcome definitions, rigorous internal validation, external validation, calibration assessment, and evaluation of clinical utility is required before clinical implementation.