Predicting Critical Failure Zones in Dental Implants: A Comparison of MLP and Random Forest Classifiers

Dental implants have excellent clinical results, but they still face a significant engineering hurdle: mechanical failure from repeated loading. Finite element simulations are widely used to identify areas of elevated stress in implant structures, but their computational cost makes them impractical for exhaustive scenario testing. This study proposes an artificial intelligence-based solution for rapidly predicting biomechanically critical conditions in dental implants. Specifically, two machine learning classifiers—a multilayer perceptron neural network and a Random Forest—were developed and compared. A dataset of 200 simulations was generated using finite element analysis by varying implant diameter, loading angle, and force magnitude. For each case, three biomechanical features were extracted: maximum von Mises stress, equivalent deformation, and fatigue safety factor. Risk cases were labeled based on a fatigue safety factor threshold. The neural network consisted of two hidden layers, while the Random Forest model comprised 100 decision trees. Both models were trained on 80% of the data and validated on the remaining 20%. The neural network achieved 99% classification accuracy, while the Random Forest reached 100%. The neural model demonstrated better sensitivity in identifying failure-prone scenarios, whereas the Random Forest provided better interpretability through feature importance analysis. These results highlight how artificial intelligence can be effectively integrated into the engineering workflow to support failure risk assessment in implant design and planning. The proposed surrogate models significantly reduce computation time and enable scalable, biomechanically informed decision-making.