Does mandibular advancement ortho-surgical procedure cause condyle changes? A case-series analysis.

Introduction: Mandibular advancement surgery corrects bone bases while establishing patients' functional and aesthetic rehabilitation. However, little is known about the results of this procedure in the structures that make up the stomatognathic system, as the condyles.

Objective: This study aimed to evaluate the structural and positional changes of mandibular condyles in ortho-surgical patients who underwent mandibular advancement surgery.

Comparison of two CBCT analysis techniques with conventional periapical radiographs in assessment of tertiary dentin after indirect pulp capping in young permanent teeth.

Background And Aim: To compare two cone beam computed tomography (CBCT) analysis techniques for measuring tertiary dentin (TD) volume, density, and root length increase, after indirect pulp therapy (IPT) in young permanent teeth with conventional periapical radiographs.

Design: Comparative study design: Sixty-nine CBCT scans were taken initially (T1) and after 1 year (T2) of IPT. New CBCT analysis technique A, standardization, segmentation, and registration of T1 and T2 scans were performed using ITK-SNAP and 3D Slicer CMF to measure TD volume (mm), density (gray-level intensity), and root length increase (mm).

Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.

Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3° and <2mm of angular and linear errors when compared to expert clinicians).

DENTALMODELSEG: FULLY AUTOMATED SEGMENTATION OF UPPER AND LOWER 3D INTRA-ORAL SURFACES.

In this paper, we present a deep learning-based method for surface segmentation. This technique consists of acquiring 2D views and extracting features from the surface such as the normal vectors. The rendered images are analyzed with a 2D convolutional neural network, such as a UNET.