Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.

Objective(s): This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools.

Materials And Methods: Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment.

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR.

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians.

Automatic Segmentation of Dental Root Canal and Merging with Crown Shape.

In this paper, machine learning approaches are proposed to support dental researchers and clinicians to study the shape and position of dental crowns and roots, by implementing a Patient Specific Classification and Prediction tool that includes RootCanalSeg and DentalModelSeg algorithms and then merges the output of these tools for intraoral scanning and volumetric dental imaging. RootCanalSeg combines image processing and machine learning approaches to automatically segment the root canals of the lower and upper jaws from large datasets, providing clinical information on tooth long axis for orthodontics, endodontics, prosthodontic and restorative dentistry procedures. DentalModelSeg includes segmenting the teeth from the crown shape to provide clinical information on each individual tooth.

Dental long axes using digital dental models compared to cone-beam computed tomography.

Objective: Standard methods of evaluating tooth long axes are not comparable (digital dental models [DDMs], panoramic and cephalometric radiographs) or expose patients to more radiation (cone-beam computed tomography [CBCT]). This study aimed to compare angular changes in tooth long axes using DDMs vs using CBCTs.

Settings And Sample Population: Secondary data analysis of DDMs and CBCTs, taken before and after orthodontic treatment with piezocision of 24 patients.

FlyBy CNN: A 3D surface segmentation framework.

In this paper, we present FlyBy CNN, a novel deep learning based approach for 3D shape segmentation. FlyByCNN consists of sampling the surface of the 3D object from different view points and extracting surface features such as the normal vectors. The generated 2D images are then analyzed via 2D convolutional neural networks such as RUNETs.

Buccal bone defects and transversal tooth movement of mandibular lateral segments in patients after orthodontic treatment with and without piezocision: A case-control retrospective study.

Introduction: This study aimed to compare the extent of buccal bone defects (dehiscences and fenestrations) and transversal tooth movement of mandibular lateral segments in patients after orthodontic treatment with and without piezocision in cone-beam computed tomography and digital dental models.

Methods: The study sample of this study consisted of cone-beam computed tomography scans and digital dental models taken before (T0) and after (T1) orthodontic treatment of 36 patients with moderate mandibular anterior crowding. The experimental group consisted of 17 patients that had piezocision performed at the beginning of treatment with the goal of accelerating tooth movement, which was compared with 19 patients who did not receive piezocision.

Patient Specific Classification of Dental Root Canal and Crown Shape.

This paper proposes machine learning approaches to support dentistry researchers in the context of integrating imaging modalities to analyze the morphology of tooth crowns and roots. One of the challenges to jointly analyze crowns and roots with precision is that two different image modalities are needed. Precision in dentistry is mainly driven by dental crown surfaces characteristics, but information on tooth root shape and position is of great value for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry.

Comparison of linear and angular changes assessed in digital dental models and cone-beam computed tomography.

Objective: To compare the three-dimensional (3D) linear displacements and the mesiodistal and buccolingual angulation changes after orthodontic treatment in digital dental models (DDMs) and 3D models derived from cone-beam computed tomography (CBCT).

Settings And Sample Population: Digital dental model and CBCT scans were selected from 24 adults who had undergone orthodontic treatment for mandibular anterior crowding.

Material And Methods: 3D linear displacements and changes in angular measurements (mesiodistal and buccolingual angulation) were assessed in pre- and post-treatment DDM and CBCT images using the software ITK-snap and 3D SlicerCMF.

Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis.

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians.

Challenges in measuring angles between craniofacial structures.

Objective: Three-dimensional (3D) angular measurements between craniofacial planes pose challenges to quantify maxillary and mandibular skeletal discrepancies in surgical treatment planning. This study aims to compare the reproducibility and reliability of two modules to measure angles between planes or lines in 3D virtual surface models.

Methodology: Twenty oriented 3D virtual surface models de-identified and constructed from CBCT scans were randomly selected.

A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis.

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects.

Accuracy and reliability of mandibular digital model registration with use of the mucogingival junction as the reference.

Objective: The aim of the study was to validate a method of mandibular digital model (DM) registration, acquired from an intraoral scanner, compared with high-resolution voxel-based cone beam computed tomography (CBCT) registration with use of the mucogingival junction as the reference.

Study Design: Pre- and post-treatment CBCT and DM images from 12 adults were randomly selected from an initial sample of 40 patients who had undergone orthodontic treatment. The DM registration was performed in 6 steps: (1) construction of 3-dimensional (3-D) volumetric label maps of CBCT scans, (2) voxel-based registration of CBCT scans, (3) prelabeling of CBCT images, (4) approximation and registration of DM models to the corresponding CBCT models, (5) mucogingival-junction registration of pretreatment and post-treatment DM images, and (6) measurements.

Volumetric reconstruction and determination of minimum crosssectional area of the pharynx in patients with cleft lip and palate: comparison between two different softwares.

Objective: The aim of this study was to assess the accuracy of volumetric reconstruction of the pharynx by comparing the volume and minimum crosssectional area (mCSA) determined with open-source applications (ITK-Snap, www.itksnap.org ; SlicerCMF) and commercial software (Dolphin3D, 11.

A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA).

Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA.