Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images.

The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image.

Full virtual patient generated by artificial intelligence-driven integrated segmentation of craniomaxillofacial structures from CBCT images.

Objectives: To assess the performance, time-efficiency, and consistency of a convolutional neural network (CNN) based automated approach for integrated segmentation of craniomaxillofacial structures compared with semi-automated method for creating a virtual patient using cone beam computed tomography (CBCT) scans.

Methods: Thirty CBCT scans were selected. Six craniomaxillofacial structures, encompassing the maxillofacial complex bones, maxillary sinus, dentition, mandible, mandibular canal, and pharyngeal airway space, were segmented on these scans using semi-automated and composite of previously validated CNN-based automated segmentation techniques for individual structures.

Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

Objectives: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images.

Methods: A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30).

CBCT for Diagnostics, Treatment Planning and Monitoring of Sinus Floor Elevation Procedures.

Sinus floor elevation (SFE) is a standard surgical technique used to compensate for alveolar bone resorption in the posterior maxilla. Such a surgical procedure requires radiographic imaging pre- and postoperatively for diagnosis, treatment planning, and outcome assessment. Cone beam computed tomography (CBCT) has become a well-established imaging modality in the dentomaxillofacial region.

Three-dimensional quantification of skeletal midfacial complex symmetry.

Purpose: Quantification of skeletal symmetry in a healthy population could have a strong impact on the reconstructive surgical procedures where mirroring of the contralateral healthy side acts as a clinical reference for the restoration of unilateral defects. Hence, the aim of this study was to three-dimensionally assess the symmetry of skeletal midfacial complex in skeletal class I patients.

Methods: A sample of 100 cone beam computed tomography (CBCT) scans (50 males, 50 females; age range: 19-40 years) were recruited.

Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images.

Objective: To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images.

Materials And Methods: A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition.

Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.

An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset. Recently, convolutional neural networks (CNNs) have proven to provide excellent performance in the field of 3D image analysis.

Impact of orthognathic surgery on root resorption: A systematic review.

Objective: This systematic review was performed to assess the potential influence of orthognathic surgery on root resorption (RR).

Material And Methods: An electronic search was conducted using PubMed, Web of Science, Cochrane Central and Embase for articles published up to April 2022. Following inclusion and exclusion criteria, a total of six articles were selected that reported on RR following orthognathic surgery.

Three-dimensional facial hard tissue symmetry in a healthy Caucasian population group: a systematic review.

Objective: The aim of this study was to quantify the symmetry of the facial hard tissue structures using three-dimensional radiographic imaging modalities in a normal Caucasian population group.

Materials And Methods: Electronic literature search was conducted in the following databases: PubMed, Embase, Web of Science, and Cochrane Library up to February 2021. The studies assessing symmetry of facial bones using computed tomography (CT) and cone beam CT were included.