Enhancing predictive analytics in mandibular third molar extraction using artificial intelligence: A CBCT-Based study

Abstract

Objective

Forecasting the complexity of extracting mandibular third molars is crucial for selecting appropriate surgical methods and minimizing postoperative complications. This study aims to develop an AI-driven predictive model using CBCT reports, focusing specifically on predicting the difficulty of mandibular third molar extraction.

Methods

We conducted a retrospective study involving 738 CBCT reports of mandibular third molars. The data was divided into a training set consisting of 556 reports and a validation set containing 182 reports. The study involved two main steps: pre-processing and processing of the textual data. During pre-processing, the reports were cleaned and standardized. In the processing phase, a rule-based NLP algorithm was employed to identify relevant features such as angulation, number of roots, root curvature, and root-nerve canal relationship. These features were utilized for the training of a deep learning neural network to classify the extraction difficulty into four categories: easy, slightly difficult, moderately difficult, and very difficult.

Results

The classification model achieved an accuracy of 95% in both the training and validation sets. Precision, recall, and F1-score metrics were calculated, yielding promising results with precision and recall values of 0.97 and 0.95 for the training set, and 0.97 and 0.89 for the validation set, respectively.

Conclusion

The study demonstrated the high reliability of AI-based models to forecast the complexity of the mandibular third molar extractions from CBCT reports. The results indicate that AI-driven models can accurately predict extraction difficulty, thereby aiding clinicians in making informed decisions and potentially improving patient outcomes.