Unsupervised tooth segmentation from three dimensional scans of the dental arch using domain adaptation of synthetic data.

Background: The automated segmentation of individual teeth from 3D models of the human dental arch is challenging due to variations in tooth alignment, arch form and overall maxillofacial anatomy. Domain adaptation is a specialised technique in deep learning which allows models to adapt to data from different domains, such as varying tooth and dental arch forms, without requiring human annotations.

Purpose: This study aimed to segment individual teeth from various dental arch morphologies in 3D intraoral scans using domain adaptation.

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.

Objectives: Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function.

Application of 3D neural networks and explainable AI to classify ICDAS detection system on mandibular molars.

Statement Of Problem: Considerable variations exist in cavity preparation methods and approaches. Whether the extent and depth of cavity preparation because of the extent of caries affects the overall accuracy of training deep learning models remains unexplored.

Purpose: The purpose of this study was to investigate the difference in 3-dimensionsal (3D) model cavity preparations after International Caries Detection and Assessment System (ICDAS) classification performed by different practitioners and the subsequent influence on the ability of a deep learning model to predict cavity classification.

Characterisation of 3D Printable Filaments Subjected to Edible Liquids: An Analysis of Fused Deposition Modelling for Intraoral Applicability.

Purpose: To evaluate the influence of edible liquids on the characteristic properties of 3D printable materials compared to conventionally used dental resin acrylic.

Method: Dental polymethyl methacrylate (PMMA) specimens were fabricated from preformed molds while polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) specimens were 3D printed using fused deposition modelling at 0.1 mm layer thickness.

Relationship between anterior occlusion, arch dimension, and mandibular movement during speech articulation: A three-dimensional analysis.

Statement Of Problem: Studies correlating occlusal morphology from 3-dimensional intraoral scans with both soft and hard tissue dynamic landmark tracking within the same participant population are lacking.

Purpose: The purpose of this clinical study was to use 3-dimensional intraoral scanning, computer-aided design, electrognathography, and artificial intelligence to investigate the relationships between anterior occlusion and arch parameters with hard and soft tissue displacements during speech production.

Material And Methods: An artificial intelligence (AI) driven software program and electrognathography was used to record the phonetic activities in 62 participants for soft tissue (ST) and hard tissue (HT) displacement.

Predictive modelling of freeway space utilising clinical history, normalised muscle activity, dental occlusion, and mandibular movement analysis.

This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable.

Predicting masticatory muscle activity and deviations in mouth opening from non-invasive temporomandibular joint complex functional analyses.

Background: A quantitative approach to predict expected muscle activity and mandibular movement from non-invasive hard tissue assessments remains unexplored.

Objectives: This study investigated the predictive potential of normalised muscle activity during various jaw movements combined with temporomandibular joint (TMJ) vibration analyses to predict expected maximum lateral deviation during mouth opening.

Method: Sixty-six participants underwent electrognathography (EGN), surface electromyography (EMG) and joint vibration analyses (JVA).

A comparison of a handheld minicomputer and an external graphics processing unit in performing 3D intraoral scans.

Statement Of Problem: Whether the use of an external graphics processing unit (eGPU) and a handheld computer prolongs the operation time for 3-dimensional (3D) intraoral scanning or produces clinically unacceptable scans is unclear.

Purpose: The purpose of this in vitro study was to compare the 3D intraoral scan accuracy and scan time of a small portable device and an eGPU with desktop-grade workstations.

Material And Methods: A handheld computer, a laptop, a desktop workstation, and an external graphics card were used to scan a 3D printed set of maxillary and mandibular casts 10 consecutive times using an intraoral scanner.

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations.

Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers.

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions.

Clinical Annotation and Segmentation Tool (CAST) Implementation for Dental Diagnostics.

Purpose This study aims to document the early stages of development of an unsupervised, deep learning-based clinical annotation and segmentation tool (CAST) capable of isolating clinically significant teeth in both intraoral photographs and their corresponding oral radiographs. Methods The dataset consisted of 172 intraoral photographs and 424 dental radiographs, manually annotated by two operators, augmented to yield 6258 images for training, 183 for validation, and 98 for testing. The training involved the use of an object detection model ('YOLOv8') combined with a feature extraction system ('Segment Anything Model').

Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study.

Purpose: 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.

Methods: Twenty-two dental casts were scanned using two handheld intraoral scanners and one laboratory scanner, resulting in 110 3D cast scans across five independent groups.

Digital Shade Matching in Dentistry: A Systematic Review.

The pursuit of aesthetic excellence in dentistry, shaped by societal trends and digital advancements, highlights the critical role of precise shade matching in restorative procedures. Although conventional methods are prevalent, challenges such as shade guide variability and subjective interpretation necessitate a re-evaluation in the face of emerging non-proximity digital instruments. This systematic review employs PRISMA protocols and keyword-based search strategies spanning the Scopus, PubMed.

Heritability of dental arches and occlusal characteristics: a systematic review and meta-analysis.

Background And Objective: The genetic basis of dentoalveolar characteristics has been investigated by several studies, however, the findings are equivocal. The objective of this systematic review and meta-analysis was to evaluate the heritability of dental arches and occlusal parameters in different stages of human dentition.

Search Methods: Electronic databases PubMed, Embase, Scopus, Web of Science, and Dentistry and Oral Science Source were searched up to August 2023 without the restriction of language or publication date.

A 3D printed electronic wearable device to generate vertical, horizontal and phono-articulatory jaw movement parameters: A concept implementation.

Purpose: The current research aimed to develop a concept open-source 3D printable, electronic wearable head gear to record jaw movement parameters.

Materials & Methods: A 3D printed wearable device was designed and manufactured then fitted with open-source sensors to record vertical, horizontal and phono-articulatory jaw motions. Mean deviation and relative error were measured invitro.

Neuroendocrine Influencers and Associated Factors That Shape Jaw Movement and Growth in Temporomandibular Joint Disorder Management: A Systematic Review of Clinical and Radiographic Evidence.

Objective: To investigate the influence of endogenous and exogenous neuroendocrine analogues on the range and motion of jaw movement, mandibular growth, and factors affecting condylar guidance in patients with temporomandibular joint disorders using clinical assessment and radiographic imaging.

Material And Methods: Eligible articles were extracted from eleven databases in early 2023 and screened following PRISMA protocols. Certainty of evidence and potential biases were assessed using the GRADE approach.

Experimental validation of computer-vision methods for the successful detection of endodontic treatment obturation and progression from noisy radiographs.

Purpose: (1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics.

Methods: The study conformed to the STARD 2015 and MI-CLAIMS 2021 guidelines.

Visual Diagnostics of Dental Caries through Deep Learning of Non-Standardised Photographs Using a Hybrid YOLO Ensemble and Transfer Learning Model.

Background: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy.

Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review.

Objective: This review aimed to systematically analyse the influence of clinical variables, diagnostic parameters and the overall image acquisition process on automation and deep learning in TMJ disorders.

Methods: Articles were screened in late 2022 according to a predefined eligibility criteria adhering to the PRISMA protocol. Eligible studies were extracted from databases hosted by MEDLINE, EBSCOHost, Scopus, PubMed and Web of Science.

Clinical machine learning in parafunctional and altered functional occlusion: A systematic review.

Statement Of Problem: The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking.

Purpose: The purpose of this study was to systematically critique the digital methods and techniques used to deploy automated diagnostic tools in the clinical evaluation of altered functional and parafunctional occlusion.

Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation.

The current 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. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = - 0.

Variables influencing the device-dependent approaches in digitally analysing jaw movement-a systematic review.

Background: To explore the digitisation of jaw movement trajectories through devices and discuss the physiological factors and device-dependent variables with their subsequent effects on the jaw movement analyses.

Methods: Based on predefined eligibility criteria, the search was conducted following PRISMA-P 2015 guidelines on MEDLINE, EBSCO Host, Scopus, PubMed, and Web of Science databases in 2022 by 2 reviewers. Articles then underwent Cochrane GRADE approach and JBI critical appraisal for certainty of evidence and bias evaluation.

Color variations during digital imaging of facial prostheses subjected to unfiltered ambient light and image calibration techniques within dental clinics: An in vitro analysis.

Background: The study aimed to evaluate 1) the amount of color variations presents within clinical images of maxillofacial prosthetic silicone specimens when photographed under different clinically relevant ambient lighting conditions, and 2) whether white balance calibration (WBC) methods were able to mitigate variations in ambient lighting.

Methods: 432 measurements were acquired from standardized images of the pigmented prosthetic silicone specimens within different ambient lighting conditions (i.e.

Digital workflow and virtual validation of a 3D-printed definitive hollow obturator for a large palatal defect.

This clinical report describes how a hollow obturator prosthesis was designed and fabricated for an 82-year-old partially edentulous patient with a large palatal defect. Computer-aided design (CAD) was used to design, articulate, and align the mandibular denture with the obturator prosthesis. The prosthesis was printed, adjusted chairside, rescanned, and made hollow by using a CAD software program.

The loss of an eye due to occupational injury: Two case studies of ocular prosthetic rehabilitation.

Background: Two patients received ocular injuries from rusted metallic projectiles at their industrial workplaces. Said injuries resulted in the loss of their eyes by evisceration surgeries to prevent fatal infections.

Case Description: The first case, a man in his twenties, received a stock conformer immediately after surgery and started prosthetic therapy within 2 months.