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. The casts were provided by the manufacturers of the scanner, and the scanning process was conducted by a single operator following best-practice methods. The time required to scan and process the 3D models was analyzed via 1-way ANOVA. Dimensional similarity was assessed using the Hausdorff distance (HD) across the resultant 80 independent bimaxillary 3D scans. A dental desktop 3D scanner was used to scan the casts which served as the control reference. HD values were analyzed via multifactorial ANOVA (α=.05). RESULTS: In the real-time rendering of 3D intraoral scans, the laptop without an eGPU took significantly longer (146.41 ±10.66 seconds) (F=30.58, P<.001) compared with when connected to an eGPU (117.66 ±6.95 seconds) and handheld computer (114.84 ±7.20 seconds). Postprocessing times were more favorable on the desktop workstation (16.61 ±4.18 seconds) compared with the laptop with (27.85 ±8.89 seconds) and without an eGPU (32.37 ±7.16 seconds) connected, with the handheld computer and eGPU combination (14.66 ±7.37 seconds) producing the best results (F=14.60, P<.001). Dimensional similarity assessments showed high consistency (F=0.92, P=.44), with no discrepancies noted on the prepared tooth surfaces. The handheld minicomputer with an eGPU produced the best results across all 4 groups. CONCLUSIONS: The handheld computer with an eGPU offered 3D intraoral scans comparable with output from a traditional workstation while preserving the details on the tooth preparations but at significantly faster scanning and processing rates.

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. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity.

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. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

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'). This combination enabled the auto-annotation and segmentation of tooth-related features and lesions in both types of images without operator intervention. Outputs were further processed using a data relabelling tool ('X-AnyLabeling') enabling the option to manually reannotate erroneous data outputs through reinforcement learning. Results The trained object detection model achieved a mean average precision (mAP) of 77.4%, with precision and recall rates of 75.0% and 72.1%, respectively. The model was able to segment features from oral images annotated by polygonal boundaries better than radiological images annotated using bounding boxes. Conclusion The development of the auto-annotation and segmentation tool showed initial promise in automating the image labelling and segmentation process for intraoral images and radiographs. Further work is required to address the limitations.

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.gov, and Web of ScienceTM databases, with the last updated search carried out in October 2023. The study aimed to synthesise literature that identified digital non-proximity recording instruments and associated colour spaces in dentistry and compare the clinical outcomes of digital systems with spectrophotometers and conventional visual methods. Utilising predefined criteria and resolving disagreements between two reviewers through Cohen's kappa calculator, the review assessed 85 articles, with 33 included in a PICO model for clinical comparisons. The results reveal that 42% of studies employed the CIELAB colour space. Despite the challenges in study quality, non-proximity digital instruments demonstrated more consistent clinical outcomes than visual methods, akin to spectrophotometers, emphasising their efficacy in controlled conditions. The review underscores the evolving landscape of dental shade matching, recognising technological advancements and advocating for methodological rigor in dental research.

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. The scans underwent uniform augmentation and were validated using Hausdorff's distance (HD) and root mean squared error (RMSE), with the laboratory scanner as reference. A 3-factor analysis of variance examined interactions between scanners, operators, and augmentation methods. Scans were divided into training and validation sets and processed through a pretrained 3D visual transformer, and validation accuracy was assessed for each of the five groups. Results: No significant differences in HD and RMSE were found across handheld scanners and operators. However, significant changes in RMSE were observed between native and augmented scans with no specific interaction between scanner or operator. The 3D visual transformer achieved 96.2% validation accuracy for differentiating upper and lower scans in the augmented dataset. Native scans lacked volumetric depth, preventing their use for deep learning. Conclusion: Scanner, operator, and processing method did not significantly affect the dimensional accuracy of 3D scans for unsupervised deep learning. However, data augmentation was crucial for processing intraoral scans in deep learning algorithms, introducing structural differences in the 3D scans. Clinical Significance. The specific type of intraoral scanner or the operator has no substantial influence on the quality of the generated 3D scans, but controlled data augmentation of the native scans is necessary to obtain reliable results with unsupervised deep learning.

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. The device was implemented on two volunteers for the parameters of maximum anterior protrusion (MAP), maximum lateral excursion (MLE), normal (NMO), and maximum (MMO) mouth opening and fricative phono-articulation. Raw data was normalized using z-score and root mean squared error (RMSE) values were used to evaluate relative differences in readings across the two participants. RESULTS: RMSE differences across the left and right piezoresistive sensors demonstrated near similar bilateral movements during normal (0.12) and maximal mouth (0.09) opening for participant 1, while varying greatly for participant 2 (0.25 and 0.14, respectively). There were larger differences in RMSE during accelerometric motion in different axes for MAP, MLE and Fricatives. CONCLUSION: The current implementation demonstrated that a 3D printed electronic wearable device with open-source sensor technology can record horizontal, vertical, and phono-articulatory maxillomandibular movements in two participants. However, future efforts must be made to overcome the limitations documented within the current experiment.

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. Machining-induced surface fracture topographies were measured with a 3D white light optical profilometer using the arithmetic mean, peak and valley, and maximum heights, as well as the kurtosis and skewness height distributions, and the texture aspect ratios. Fracture morphologies were analysed using scanning electron microscopy (SEM). The surface fracture topographies were significantly dependent on the material microstructure, the mechanical properties, and the ultrasonic machining vibration amplitudes. Larger scale fractures with higher arithmetic mean, peak and valley heights, and kurtosis and skewness height distributions were induced in higher brittleness indexed pre-crystallized ZLS than lower indexed crystallized ZLS by conventional machining. Conchoidal fractures occurred in pre-crystallized ZLS while microcracks were found in crystallized state although brittle fractures mixed with localized ductile flow deformations dominated all machined ZLS surfaces. Ultrasonic machining at an ideal vibration amplitude resulted in more ductile removal, reducing fractured-induced peaks and valleys for both materials than conventional processing. This research demonstrates the microstructure-property-processing interdependence for ZLS materials and the novel machining technique to be superior to current processing, reducing fractures in the materials and potentially advancing dental CAD/CAM techniques.

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

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.

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. The data was categorized and presented in tabulated and chart form using Microsoft Excel software (version 2016). Paired t-tests and Mann-Kendall trend tests were performed to analyze for statistical significance between the different restoration types across the program completions. Results: Porcelain bonded to metal (PBM) crowns represented 42.05% of all fixed prosthodontics units completed over all study years followed by all-ceramic crowns (ACC) (18.14%) and full gold crowns (FGC) (10.70%). Jointly, PBM, ACC and FGC's encompassed 70.88% of all fixed prosthodontics units. Over the 8-year study period, there were observed trends of reduced use of PBM's, increased use of ACC's, statistically significant reduced use of FGC's (p = 0.035) and a statistically significant difference in the use of complete and partial coverage restorations (p < 0.001). Conclusion: PBM crowns were the dominant laboratory fabricated fixed prosthodontic clinical unit across postgraduate prosthodontics program completions. The trend in later years indicating ACC as the dominant crown type warrants further investigation.

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. Each implant-abutment complex was positioned within a tooth wear machine and subjected to 1,000,000 cycles of 50-Ncm nonaxial loading to simulate 1 year of function. The abutment screw removal torque values were measured before and after cyclic loading, and the differences were statistically analyzed using two-way ANOVA and post hoc pairwise Dunn tests. Scanning electron microscopy and finite element analyses were performed to assess the wear of the abutment screws. RESULTS: The mean torque loss for the AI group was 21.44% (P < .001) compared to 24.56% (P < .001) for the SI group. There was a statistically significant difference between the AI and SI groups (P = .006). CONCLUSION: Both groups exhibited significant abutment screw loosening. Within the limitations of this study, 24-degree angulation-correcting implants resisted screw loosening significantly more than straight implants.

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. RESULTS: Nineteen articles were screened, with four deemed to be of high quality, eight of moderate quality, and the remaining seven of low to very low quality. Corticosteroids improve maximal incisal opening but not TMJ disorder symptoms. Higher doses worsen jaw movement and cause osseous deformity. Growth hormone affects occlusal development, and delayed treatment affects arch width. Sex hormone correlation with TMJ disorder is complex, with some studies showing a correlation between menstrual cycle phases and pain/limited mobility. CONCLUSIONS: The evaluation of neuroendocrine influencers in relation to jaw movement in patients with temporomandibular joint disorders involves the complex interplay of potentially confounding factors that each require careful consideration to ensure accurate diagnoses and evaluations.

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. 250 deidentified dental radiographs were collected and augmented to produce 2226 images. The dataset was classified according to endodontic treatment outcomes following a set of customized criteria. The dataset was denoised and balanced, and processed with YOLOv5s, YOLOv5x, and YOLOv7 models of real-time deep-learning computer vision. Diagnostic test parameters such as sensitivity (Sn), specificity (Sp), accuracy (Ac), precision, recall, mean average precision (mAP), and confidence were evaluated. RESULTS: Overall accuracy for all the deep-learning models was above 85%. Imbalanced datasets with noise removal led to YOLOv5x's prediction accuracy to drop to 72%, while balancing and noise removal led to all three models performing at over 95% accuracy. mAP saw an improvement from 52 to 92% following balancing and denoising. CONCLUSION: The current study of computer vision applied to radiomic datasets successfully classified endodontic treatment obturation and mishaps according to a custom progressive classification system and serves as a foundation to larger research on the subject matter.

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. METHODS: A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The "you only look once" algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP). RESULTS: The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs. CONCLUSION: A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications.

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. MATERIAL AND METHODS: Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles were critically appraised by using the Joanna Briggs Institute's Diagnostic Test Accuracy (JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist. RESULTS: Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained via radiographs and photographs produced notable errors in prediction accuracy. While half of the studies adhered to robust methods of computer science, the lack of blinding to a reference standard and convenient exclusion of data in favor of accurate machine learning suggested that conventional diagnostic test methods were ineffective in regulating machine learning research in clinical occlusion. As preestablished baselines or criterion standards were lacking for model evaluation, a heavy reliance was placed on the validation provided by clinicians, often dental specialists, which was prone to subjective biases and largely governed by professional experience. CONCLUSIONS: Based on the findings and because of the numerous clinical variables and inconsistencies, the current literature on dental machine learning presented nondefinitive but promising results in diagnosing functional and parafunctional occlusal parameters.

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. Critical appraisals were performed on individual studies following Nature Medicine's MI-CLAIM checklist while a combined appraisal of the image acquisition procedures was conducted using Cochrane's GRADE approach. RESULTS: Twenty articles were included for full review following eligibility screening. The average experience possessed by the clinical operators within the eligible studies was 13.7 years. Bone volume, trabecular number and separation, and bone surface-to-volume ratio were clinical radiographic parameters while disc shape, signal intensity, fluid collection, joint space narrowing and arthritic changes were successful parameters used in MRI-based deep machine learning. Entropy was correlated to sclerosis in CBCT and was the most stable radiomic parameter in MRI while contrast was the least stable across thermography and MRI. Adjunct serum and salivary biomarkers, or clinical questionnaires only marginally improved diagnostic outcomes through deep learning. Substantial data was classified as unusable and subsequently discarded owing to a combination of suboptimal image acquisition and data augmentation procedures. Inadequate identification of the participant characteristics and multiple studies utilising the same dataset and data acquisition procedures accounted for serious risks of bias. CONCLUSION: Deep-learned models diagnosed osteoarthritis as accurately as clinicians from 2D and 3D radiographs but, in comparison, performed poorly when detecting disc disorders from MRI datasets. Complexities in clinical classification criteria; non-standardised diagnostic parameters; errors in image acquisition; cognitive, contextual or implicit biases were influential variables that generally affected analyses of inflammatory joint changes and disc disorders.

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) = - 0.01 (10), mean difference = - 0.058, P > 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm3) compared to desktop laser scanning (322.70 ± 40.15 mm3). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area (P < 0.001), volume (P < 0.001), and spatial overlap (P < 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68-0.87, sensitivity of 1.00, precision of 0.50-0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.

Influence of a major pre-clinical programme restructure on undergraduate student fixed prosthodontics clinical unit completions.

INTRODUCTION: Research is limited in measuring the effectiveness of pre-clinical programmes in preparing students for fixed prosthodontics clinical practice. The aim of this retrospectively study was to assess the influence of a major pre-clinical programme restructure on undergraduate student fixed prosthodontics clinical unit completions. MATERIALS AND METHODS: The fixed prosthodontics treatment registers from 2011 to 2020 were reviewed, and units completed per student (UCS) and units completed per student per session (UCSS) were calculated in the years before (2011-2013) and after (2014-2020) a major pre-clinical programme restructure (PR). Data were summarised in Microsoft Excel software (version 2016), and Student's t-test and paired t-tests were performed to determine the significance of difference in UCS and UCSS in the years before and after the PR. RESULTS: There was a significant difference in the UCS (p < .05) and UCSS (p < .01) in the years before and after the PR. The average UCS in the years before the PR was 2.20 units compared with 3.86 units after the PR, an increase of 75% per student. The average UCSS in the years before the PR was 0.15 units compared with 0.28 units after the PR, an increase of 87% per session. CONCLUSION: The fixed prosthodontics pre-clinical programme restructure resulted in statistically significantly increased student clinical unit completions.

A comparative evaluation of student experience with artificial teeth used in pre-clinical fixed prosthodontics post-core undergraduate teaching programs.

INTRODUCTION: Research is lacking in the use of artificial teeth for post-core techniques in undergraduate fixed prosthodontics pre-clinical education. The aim of this study was to compare the reasons for selection and explore the direct student experiences with artificial teeth used in two pre-clinical fixed prosthodontics post-core technique teaching programs conducted in consecutive years. MATERIALS AND METHODS: Fourth year undergraduate dental students who had completed the fixed prosthodontics pre-clinical program were invited to complete an anonymous online surveys. Information was requested on the use and direct student experiences with artificial and natural teeth for post-core techniques. Quantitative data was summarised and qualitative data was clustered into topics. The reasons for selection and use of artificial and natural teeth were compared within and between the two programs. RESULTS: 36% of 70 respondents in 2020 chose to use one or more artificial teeth for the post-core exercises in the pre-clinical program compared with 94% of 77 respondents in 2021 (p < .05). The use was driven by difficulty in sourcing appropriate natural teeth. Respondents reported "ease of use" as the dominant positive user experience with the main negatives being "unrealistic simulation of natural teeth" and "different surface texture/feel when cutting." CONCLUSION: Artificial teeth were reported to provide an appropriate and realistic simulation experience compared with extracted natural teeth and were easier to source. Students focussed on the practicalities of sourcing artificial teeth, associated costs and ease of use ahead of conceivable educational benefits. Cost limited the more widespread use of artificial teeth.

Fixed prosthodontics clinical unit completions in an undergraduate curriculum: A 10-year retrospective study.

INTRODUCTION: Providing undergraduate dental students with a range of clinical experiences in fixed prosthodontics procedures is an important component of dental education. The aim of this retrospective study was to analyse the types of laboratory-fabricated fixed prosthodontics clinical units completed by undergraduate students over 10 consecutive years and determine any significant trends. MATERIALS AND METHODS: The fixed prosthodontics treatment registers from 2012 to 2021 were reviewed and the different types of completed fixed prosthodontics units and total number of fixed prosthodontics units were recorded for each year. Completed units were categorised according to the type of restoration and expressed in whole numbers and as a percentage of the total number of units completed in each year. The data were presented in table and graph form. Mann-Kendall tests were performed to statistically analyse for trends in the different restoration types. RESULTS: Throughout all study years, porcelain bonded to metal (PBM) crowns (48.25%, range 35.70%-59.91%) were the most frequently completed fixed prosthodontics unit followed by full gold crowns (FGC) (20.84%, range 14.89%-27.30%) and all-ceramic crowns (ACC) (12.70%, range 3.67%-24.41%). Collectively, PBM, FGC and ACC comprised 81.80% of all completed fixed prosthodontics units. There were observed trends of increased use of all types of all-ceramic containing restorations, all types of all-gold containing restorations, all types of partial coverage restorations and specifically ceramic onlays and gold onlays. There were observed trends of reduced use of cast gold post-cores and all types of bridges. CONCLUSION: PBM crowns were the mainstay laboratory-fabricated fixed prosthodontic unit completed over 10 years of undergraduate student fixed prosthodontics clinical practice.