Unsupervised tooth segmentation from three dimensional scans of the dental arch using domain adaptation of synthetic data.

Background: The automated segmentation of individual teeth from 3D models of the human dental arch is challenging due to variations in tooth alignment, arch form and overall maxillofacial anatomy. Domain adaptation is a specialised technique in deep learning which allows models to adapt to data from different domains, such as varying tooth and dental arch forms, without requiring human annotations.

Purpose: This study aimed to segment individual teeth from various dental arch morphologies in 3D intraoral scans using domain adaptation.

Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars.

Statement Of Problem: Considerable variations exist in cavity preparation methods and approaches. Whether the extent and depth of cavity preparation because of the extent of caries affects the overall accuracy of training deep learning models remains unexplored.

Purpose: The purpose of this study was to investigate the difference in 3-dimensionsal (3D) model cavity preparations after International Caries Detection and Assessment System (ICDAS) classification performed by different practitioners and the subsequent influence on the ability of a deep learning model to predict cavity classification.