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 relevan

SUBMITTER: Khorshidi F 

PROVIDER: S-EPMC11976095 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature