Isotopological remeshing and statistical shape analysis: Enhancing premolar tooth wear classification and simulation with machine learning.

Objective: The aim of this study was to evaluate the accuracy of a combined approach based on an isotopological remeshing and statistical shape analysis (SSA) to capture key anatomical features of altered and intact premolars. Additionally, the study compares the capabilities of four Machine Learning (ML) algorithms in identifying or simulating tooth alterations.

Methods: 113 premolar surfaces from a multicenter database were analyzed.

Assessment of guide fitting using an intra-oral scanner: An in vitro study.

Objectives: The aim of this study was to evaluate the capacity of an intra-oral scanner (IOS) to assess the position of an endodontic guide in vitro.

Methods: Fourteen extracted human teeth were placed into a maxillary model and scanned using computed tomography and a reference laboratory scanner. An ideal endodontic guide was then created and modified by adding defects of different thicknesses to simulate incorrect positions: 50 μm, 150 μm, 400 μm, and 1000 μm.