Percentage of mesh reduction appropriate for designing digital obturator prostheses on personal computers

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.

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.

Effects of remeshing algorithms on trueness of fit when used to compress .stl files for digital dental model: A narrative literature review

OBJECTIVES

In recent years, there has been a transition toward using and storing digitized dental models instead of physical casts. The size of .stl files is directly correlated with a need for higher computer processing power, longer operation time and a need for more storage space. Several studies explored the impact of decreasing the mesh resolution to decrease file size while maintaining trueness of fit between the original and altered files. Multiple authors suggested to compress .stl files by removing a fixed percentage of triangular faces. However, certain variables which are not yet fully investigated may impact the outcome of remeshing and compressing .stl files.

METHODS

This narrative review article explores important concepts and considerations that may have a significant impact on the outcome of remeshing and compressing .stl file.

RESULTS

When restructuring digital meshes to compress .stl files, numerous variables such as initial mesh density, adaptive resolution, scanning technology, rendition and remeshing algorithms, and the clinical situation can affect the outcome.

CONCLUSION

Prior to applying subjective compression to .stl files by a static percentage, multiple variables must be considered to ensure trueness of fit is preserved. The results obtained for specific situations may not extrapolate to others.

CLINICAL SIGNIFICANCE

Remeshing algorithms used to reduce .stl file size, or to optimize the files prior to manufacturing, may cause the loss of important data. Further research is needed to guide clinicians safely altering digital meshes.

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.

Triangular mesh reduction of digitized maxillectomy defects for prosthetic rehabilitation: A 3D deviation study

OBJECTIVES

To evaluate the effect of different amounts of triangular mesh reduction on the trueness of digitized complete-arch dentate and edentulous maxillectomy defects models.

MATERIAL AND METHODS

Twenty gypsum maxillectomy defect models (dentate and edentate group: n=10) were digitized using the Trios 3 intraoral scanner, scanning the teeth, mucosa and maxillectomy defect. These datasets (reference, R₀) were saved as standard tessellation language (STL) files, and triangular mesh reduction was performed using Meshmixer's reduction tool. Digital test-datasets with file sizes reduced by 50%(R₁), 75%(R₂), and 90%(R₃) were generated (each: n=20). Each test-dataset was compared to the R₀ file using 3D evaluation software (GOM Inspect), applying automated pre-alignment followed by a global best-fit alignment, and root mean square (RMS) 3-dimensional (3D) deviations were calculated. Statistical analyses were performed, at a level of significance of α=0.05.

RESULTS

The number of triangles, and STL file size were synchronized with each other and inversely proportional to the amount of mesh reduction. The resulting mean percentages of the STL file sizes were 50.00% for R₁, 24.93% for R₂, and 10.00% for R₃. There were no 3D deviations at 50% triangular mesh reduction. The 3D deviations increased with the amount of mesh reduction: at 75% reduction the median deviations were lower (dentate:0.0016mm, IQR:0.0015-0.0018; edentate:0.0016mm, IQR:0.0015-0.0016), than at 90% (dentate:0.004mm, IQR:0.0038-0.0041; edentate:0.003mm, IQR:0.0036-0.0039). A statistically significant increase in 3D deviations was observed with higher degrees of mesh reduction (p<0.001).

CONCLUSIONS

Triangular mesh reduction results in a significant increase in 3D deviations if the reduction is more than 75%.

CLINICAL SIGNIFICANCE

Digital models of patients with maxillectomy defects can be saved with a mesh reduction of 50% without affecting the trueness. The use of a 50% mesh reduction decreases the required storage capacity by 50%.

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 prosthesis was printed in resin with a dental 3D printer. Quantitative evaluations of clinical (prosthesis dimensions, rest, and occlusal vertical dimensions) and virtual (surface area, volume, weight, interpoint mismatches, spatial overlap) parameters found that the 3D-printed prosthesis required an additional 5% chairside modification. The greatest differences in volume (24.7% less) and weight (22.2% less) were observed when the modified obturator bulb was made hollow via CAD. Hollowing the bulb, therefore, reduced the spatial overlap in volume by 16.8%.

Development and virtual validation of a novel digital workflow to rehabilitate palatal defects by using smartphone-integrated stereophotogrammetry (SPINS)

Palatal defects are rehabilitated by fabricating maxillofacial prostheses called obturators. The treatment incorporates taking deviously unpredictable impressions to facsimile the palatal defects into plaster casts for obturator fabrication in the dental laboratory. The casts are then digitally stored using expensive hardware to prevent physical damage or data loss and, when required, future obturators are digitally designed, and 3D printed. Our objective was to construct and validate an economic in-house smartphone-integrated stereophotogrammetry (SPINS) 3D scanner and to evaluate its accuracy in designing prosthetics using open source/free (OS/F) digital pipeline. Palatal defect models were scanned using SPINS and its accuracy was compared against the standard laser scanner for virtual area and volumetric parameters. SPINS derived 3D models were then used to design obturators by using (OS/F) software. The resultant obturators were virtually compared against standard medical software designs. There were no significant differences in any of the virtual parameters when evaluating the accuracy of both SPINS, as well as OS/F derived obturators. However, limitations in the design process resulted in minimal dissimilarities. With further improvements, SPINS based prosthetic rehabilitation could create a viable, low cost method for rural and developing health services to embrace maxillofacial record keeping and digitised prosthetic rehabilitation.