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.

Bibliometric Analysis of the 100 Most-Cited Articles on the Methods of Shade-Matching in Dentistry.

Objectives: The purpose of this bibliometric analysis is to identify the 100 most cited articles and delve into citation metrics to gain insights into the evolving trends in shade-matching methods in dentistry.

Material And Methods: Following PRISMA guidelines, two reviewers conducted a structured search in Scopus using keyword-based search strings. The top 100 articles were selected based on predefined criteria, and their bibliometric data were extracted.

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.

Surface fractures in pre-crystallized and crystallized zirconia-containing lithium silicate glass-ceramics generated in ultrasonic vibration-assisted machining.

Machining-induced surface fractures in ceramic restorations is a long-standing problem in dentistry, affecting the restorations' functionality and reliability. This study approached a novel ultrasonic vibration-assisted machining technique to zirconia-containing lithium silicate glass-ceramics (ZLS) and characterized its induced surface fracture topographies and morphologies to understand the microstructure-property-processing relations. The materials were processed using a digitally controlled ultrasonic milling machine at a harmonic vibration frequency with different amplitudes.

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.

An 8-year retrospective study of fixed prosthodontics clinical unit completions in a postgraduate prosthodontics program.

Background And Aims: The range of aesthetic fixed prosthodontics materials utilizing digital manufacturing techniques has expanded in recent years ostensibly replacing traditional laboratory techniques and materials. This retrospective study conducted over eight consecutive years aimed to analyze the types of laboratory fabricated fixed prosthodontics clinical units completed in a postgraduate prosthodontics specialist training program and determine meaningful trends.

Methods: The logbooks of eight postgraduate prosthodontics completions from 2014 to 2021 were reviewed and the different types of laboratory fabricated fixed prosthodontics units and total number of fixed prosthodontics units completed were recorded.

Prosthetic management of an ocular defect utilizing a novel threaded iris fabrication technique.

Unlabelled: The conservative prosthodontic construction of an ocular prosthesis utilizing our novel threaded iris fabrication technique required high time and prosthodontic resource inputs and produced a lifelike aesthetic result.

Abstract: Patients with ocular defects frequently present with significant local anatomical deficiencies and complex histories and require extensive time and resource inputs to treat. This case report describes the conservative management of an ocular defect completed in a postgraduate prosthodontics clinical residency program utilizing a novel threaded iris fabrication technique.

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.

A Comparison of Abutment Screw Loosening in 24-Degree Angulation-Correcting and Straight Implants: An In Vitro Study.

Purpose: To compare abutment screw loosening in 24-degree angulation-correcting and straight implants subjected to nonaxial cyclic loading.

Materials And Methods: Seven external connection 24-degree angulation-correcting implants (AI) and seven external connection straight implants (SI) were embedded in acrylic resin within a brass housing. A hemispherical titanium fatigue abutment was secured to each implant using a titanium abutment screw tightened to 32 Ncm.

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.