A Multiclass Classification Model for Tooth Removal Procedures.

ABSTRACT: Surprisingly little is known about tooth removal procedures. This might be due to the difficulty of gaining reliable data on these procedures. To improve our understanding of these procedures, machine learning techniques were used to design a multiclass classification model of tooth removal based on force, torque, and movement data recorded during tooth removal. A measurement setup consisting of, among others, robot technology was used to gather high-quality data on forces, torques, and movement in clinically relevant dimensions. Fresh-frozen cadavers were used to match the clinical situation as closely as possible. Clinically interpretable variables or "features" were engineered and feature selection took place to process the data. A Gaussian naive Bayes model was trained to classify toot

SUBMITTER: de Graaf WM 

PROVIDER: S-EPMC9516607 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature