A machine learning-based predictive model for mandibular third molar extraction difficulty: incorporating multimodal features and SHAP analysis.

ABSTRACT:

Objectives

This study aimed to establish a rapid and accurate predictive model for mandibular third molar (MM3) extraction difficulty based on machine learning and multimodal parameters.

Methods

A dataset was constructed by integrating clinical characteristics with morphological features automatically extracted from cone-beam computed tomography (CBCT) images. Extraction difficulty was determined by three experienced experts using a ten-factor scoring system and clinical judgment. Six machine learning (ML) models were developed: support vector machine (SVM), artificial neural network (ANN), extreme gradient boosting (XGBoost), random forest (RF), k-nearest neighbors (KNN), and logistic regression. Model performance was optimized using grid search and five-fold cross-validati

SUBMITTER: Qiu P 

PROVIDER: S-EPMC12903583 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature