PURPOSE: The purpose of this virtual analysis study was to compare the accuracy and precision of 3-dimensional (3D) ear models generated by scanning gypsum casts with a smartphone camera and a desktop laser scanner.
MATERIAL AND METHODS: Six ear casts were fabricated from green dental gypsum and scanned with a laser scanner. The resultant 3D models were exported as standard tessellation language (STL) files. A stereophotogrammetry system was fabricated by using a motorized turntable and an automated microcontroller photograph capturing interface. A total of 48 images were captured from 2 angles on the arc (20 degrees and 40 degrees from the base of the turntable) with an image overlap of 15 degrees, controlled by a stepper motor. Ear 1 was placed on the turntable and captured 5 times with smartphone 1 and tested for precision. Then, ears 1 to 6 were scanned once with a laser scanner and with smartphones 1 and 2. The images were converted into 3D casts and compared for accuracy against their laser scanned counterparts for surface area, volume, interpoint mismatches, and spatial overlap. Acceptability thresholds were set at <0.5 mm for interpoint mismatches and >0.70 for spatial overlap.
RESULTS: The test for smartphone precision in comparison with that of the laser scanner showed a difference in surface area of 774.22 ±295.27 mm2 (6.9% less area) and in volume of 4228.60 ±2276.89 mm3 (13.4% more volume). Both acceptability thresholds were also met. The test for accuracy among smartphones 1, 2, and the laser scanner showed no statistically significant differences (P>.05) in all 4 parameters among the groups while also meeting both acceptability thresholds.
CONCLUSIONS: Smartphone cameras used to capture 48 overlapping gypsum cast ear images in a controlled environment generated 3D models parametrically similar to those produced by standard laser scanners.
MATERIALS AND METHODS: The literature search was carried out on two electronic databases (PubMed and Cochrane Library). Randomized controlled trials (RCT) published from January 2011 to September 2022 were included. The bias risk was evaluated using Cochrane Risk of Bias Tool 2.0. Further screening was done for meta-analysis according to modified Newcastle-Ottawa scoring criteria. Forest plot was generated using a statistical method of inverse variance of random effect with 95% confidence interval.
RESULTS: A total of 8 randomized controlled trials were included for systematic review out of which four studies were based on tooth-supported fixed prosthesis and remaining four were based on implant-supported prosthesis. Further screening was conducted and three studies were eligible for meta-analysis. Tooth-supported fixed prosthesis fabricated from digital impression showed no significant difference in the marginal fit in any region measured, except for occlusal region where conventional impression showed more favorable marginal fit. Implant-supported prosthesis fabricated from digital impression showed survival rates ranging from 97.3 to 100% and there was no statistically significant difference in marginal bone loss (p = 0.14).
CONCLUSION: Implant-supported prostheses fabricated from digital and conventional impressions show no significant differences in their clinical outcomes. Tooth-supported fixed prostheses fabricated from digital impression have shown favorable findings in terms of marginal fit. Despite that, there is still lack of clinical trials with larger sample size and longer follow-up periods. Future studies that fulfill these two criteria are deemed necessary.
PURPOSE: The purpose of this simulation study was to establish a reference percentage value that can be used to effectively reduce the size and polygons of the 3D mesh without drastically affecting the dimensions of the prosthesis itself.
MATERIAL AND METHODS: Fifteen different maxillary palatal defects were simulated on a dental cast and scanned to create 3D casts. Digital bulbs were fabricated from the casts. Conventional bulbs for the defects were fabricated, scanned, and compared with the digital bulb to serve as a control. The polygon parameters of digital bulbs were then reduced by different percentages (75%, 50%, 25%, 10%, 5%, and 1% of the original mesh) which created a total of 105 meshes across 7 mesh groups. The reduced mesh files were compared individually with the original design in an open-source point cloud comparison software program. The parameters of comparison used in this study were Hausdorff distance (HD), Dice similarity coefficient (DSC), and volume.
RESULTS: The reduction in file size was directly proportional to the amount of mesh reduction. There were minute yet insignificant differences in volume (P>.05) across all mesh groups, with significant differences (P
OBJECTIVE: The present study aims to assess knowledge and awareness regarding needle stick and sharp injury among dental personnel and compared the knowledge and awareness level about needle stick injuries between dental professionals and dental supporting staff working at the School of Dental Sciences, Universiti Sains Malaysia.
METHODS: This cross-sectional study was conducted from March 2016 to March 2017. The total sample included 112 registered dental personnel including dental professionals (lecturers, dental officers, and postgraduate students) and dental supportive staff (including staff nurses and dental surgery assistant) who were selected from the School of Dental Sciences, Universiti Sains Malaysia at Kelantan District of Malaysia. Data were collected by a structured questionnaire.
RESULTS: In our study 60.7% respondents answered correctly for all the statements regarding knowledge and 51.8% of the respondents answered correctly for the statements regarding awareness on needle stick and sharp injury. No significant difference of knowledge level has been observed between the dental professionals and dental supportive staff (p
MATERIALS AND METHODS: An auricular prosthesis, a complete denture, and anterior and posterior crowns were constructed using conventional methods and laser scanned to create computerized 3D meshes. The meshes were optimized independently by four computer-aided design software (Meshmixer, Meshlab, Blender, and SculptGL) to 100%, 90%, 75%, 50%, and 25% levels of original file size. Upon optimization, the following parameters were virtually evaluated and compared; mesh vertices, file size, mesh surface area (SA), mesh volume (V), interpoint discrepancies (geometric similarity based on virtual point overlapping), and spatial similarity (volumetric similarity based on shape overlapping). The influence of software and optimization on surface area and volume of each prosthesis was evaluated independently using multiple linear regression.
RESULTS: There were clear observable differences in vertices, file size, surface area, and volume. The choice of software significantly influenced the overall virtual parameters of auricular prosthesis [SA: F(4,15) = 12.93, R2 = 0.67, p < 0.001. V: F(4,15) = 9.33, R2 = 0.64, p < 0.001] and complete denture [SA: F(4,15) = 10.81, R2 = 0.67, p < 0.001. V: F(4,15) = 3.50, R2 = 0.34, p = 0.030] across optimization levels. Interpoint discrepancies were however limited to <0.1mm and volumetric similarity was >97%.
CONCLUSION: Open-source mesh optimization of smaller dental prostheses in this study produced minimal loss of geometric and volumetric details. SculptGL models were most influenced by the amount of optimization performed.
Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.
Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.
Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.
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.