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

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.