Predicting the Failure of Dental Implants Using Supervised Learning Techniques

Abstract

Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients' body conditions, surgeons' experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients' systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT), support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost) were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741); the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

Artificial intelligence applications in implant dentistry: A systematic review

“…The AI models used among the different studies are presented in Table 3. The selected articles were distributed into 3 groups depending on the application of the AI model: implant type recognition (Supplementary Table 1, available online), 13,[27][28][29][30][31][32] models to determine osteointegration success or implant success prediction by using patient risk factors and ontology criteria (Supplementary Table 2, available online), [33][34][35][36][37][38][39] and implant design optimization by combining FEA calculations and AI models (Supplementary Table 2, available online). [40][41][42] The overall accuracy outcome of the AI models developed in the different reviewed studies ranged from 93.8% to 98%.…”

“…13,[27][28][29][30][31][32] The AI models to predict osteointegration or implant success by using different input data varied among the studies ranging from 62.4% to 80.5%. [33][34][35][36][37][38][39] Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve implant designs, minimizing the stress at the implant-bone interface by 36.6% compared with the FEA model, 40 optimizing the implant design porosity, length, and diameter, improving the FEA calculations, 41 or accurately determining the elastic modulus of the implantbone interface. 42 With respect to the selection of articles by reviewing their titles and abstracts, there was significant agreement between the 2 investigators for the articles that were selected (Cohen Kappa value=0.97, P<.001) and the articles that were not selected (Cohen Kappa value=0.97, P<.001).…”

“…Regression analysis: Estimates the relationship among variables. Support Vector Machine (SVM) (classification) [33][34][35][36][37][38][39] Support Vector Regression (SVR) 40 k-Nearest Neighbors (k-NN) (supervised learning, classification) 13,36,37,39 k-Means clustering (clustering) 36 Artificial neural networks or neural networks: Linked units named artificial neurons which receives and processes a signal. The connections are called edges.…”

“…Neurons are usually collected into layers. Naïve Bayes 37 Deep Convolutional Neural Networks (CNN) [27][28][29][30][31][32] Residual Neural Network 30,32 Artificial Neural Networks (ANN) 33,[37][38][39]41,42 Decision tree learning: Prediction model using classification tree 34,35 Random forest 33 Logistic regression (LR): Used as a classification model 33,35 Multidimensional unfolding analysis (MDU) 36 Ensemble-based models: Combination of different models to improve accuracy (such as, Bagging, Adaboost) 35,37 model development and training on implant recognition. However, the clinical applicability of such an AI application would help clinicians trying to restore an unknown implant.…”

“…The main AI models used were regression analysis (support vector machine classification), decision tree learning, logistic regression, and classifier neural network. [33][34][35][36][37][38][39] However, because of a high variation in the methodologies among the different studies, comparisons among the obtained results are difficult.…”