Our work with dental 3D models

To create and validate a transformer-based deep neural network architecture for classifying 3D scans of teeth for computer-assisted manufacturing and dental prosthetic rehabilitation

to compare the 3D intraoral scan accuracy and scan time of a small portable device and an eGPU with desktop-grade workstations.

This study assessed the impact of intraoral scanner type, operator, and data augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also evaluated the validation accuracy of an unsupervised machine-learning model trained with these scans.

This multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry.