Abstract:Purpose
Mesh optimization reduces the texture quality of 3D models in order to reduce storage file size and computational load on a personal computer. This study aims to explore mesh optimization using open source (free) software in the context of prosthodontic application.
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 … Show more
“…Unlike radiomic datasets, where clinical experience is critical and dictate the overall success of machine learning 23 , both workflows in phase 2 produced respectable results when operated by 2 dentists with little to no experience with dental CAD. however, surface reconstruction for inlays while Boolean subtraction for onlays generated more reliable outcomes supporting the age-old argument of commercial software being highly optimized for smaller details while open-source or crowd supported projects being more user accessible for general projects 9 , 10 .…”
Section: Discussionmentioning
confidence: 66%
“…5 ) followed the ‘Boolean subtraction’ principles, as previously documented 36 . Virtual crown templates were obtained from scanned physical restorations on dental casts and were used to reconstruct the onlay cusps 9 , 10 , 37 . Figure 1 Workflow 1 for inlay design with respective commands: ( A ) 3D model loaded into 3-matics, ( B ) curvature analysis and curve creation on model surface, ( C ) surface reconstruction function to bridge defect , ( D ) hollow the model and trim down, ( E ) manual trimming of isolated segments, ( F ) apply wrap function, G) manually trim after wrapping, ( H ) manually trim any overhangs, ( I ) check fit onto 3D preparation model, ( J ) fine smoothing of edges, ( K ) final wrap and apply autofix function to fix any defects, ( L ) final output.…”
Section: Methodsmentioning
confidence: 99%
“…Reinforcing their proposed advantages, it has been established that gold and ceramic onlay preparations resulted in significantly less coronal tooth structure reduction compared with their full-coverage equivalents on the same tooth when performed by undergraduate students 8 , 9 . While commercially available digital solutions provided CAD assistance to dentists for digital inlay and onlay preparations, most free or open-source implementation were documented for dentures and larger prostheses as opposed to PDC 10 , 11 . Furthermore, the literature suggested that both the desktop laser scanners and intraoral scanners were accurate devices in their own rights and carried out specific features effectively 12 – 14 .…”
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.
“…Unlike radiomic datasets, where clinical experience is critical and dictate the overall success of machine learning 23 , both workflows in phase 2 produced respectable results when operated by 2 dentists with little to no experience with dental CAD. however, surface reconstruction for inlays while Boolean subtraction for onlays generated more reliable outcomes supporting the age-old argument of commercial software being highly optimized for smaller details while open-source or crowd supported projects being more user accessible for general projects 9 , 10 .…”
Section: Discussionmentioning
confidence: 66%
“…5 ) followed the ‘Boolean subtraction’ principles, as previously documented 36 . Virtual crown templates were obtained from scanned physical restorations on dental casts and were used to reconstruct the onlay cusps 9 , 10 , 37 . Figure 1 Workflow 1 for inlay design with respective commands: ( A ) 3D model loaded into 3-matics, ( B ) curvature analysis and curve creation on model surface, ( C ) surface reconstruction function to bridge defect , ( D ) hollow the model and trim down, ( E ) manual trimming of isolated segments, ( F ) apply wrap function, G) manually trim after wrapping, ( H ) manually trim any overhangs, ( I ) check fit onto 3D preparation model, ( J ) fine smoothing of edges, ( K ) final wrap and apply autofix function to fix any defects, ( L ) final output.…”
Section: Methodsmentioning
confidence: 99%
“…Reinforcing their proposed advantages, it has been established that gold and ceramic onlay preparations resulted in significantly less coronal tooth structure reduction compared with their full-coverage equivalents on the same tooth when performed by undergraduate students 8 , 9 . While commercially available digital solutions provided CAD assistance to dentists for digital inlay and onlay preparations, most free or open-source implementation were documented for dentures and larger prostheses as opposed to PDC 10 , 11 . Furthermore, the literature suggested that both the desktop laser scanners and intraoral scanners were accurate devices in their own rights and carried out specific features effectively 12 – 14 .…”
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.
“…In the orthodontic field, MeshMixer has multiple applications due to its versatility and the absence of registrations and costs [19][20][21]. MeshMixer allows the user to: Even with these advantages, and despite the relative ease of use for a user accustomed to CAD/CAM design, MeshMixer may not be easy to use for an orthodontist or dental technician with low-level computer skills (Figure 4).…”
Section: Meshmixermentioning
confidence: 99%
“…Blender (Blender Foundation, Amsterdam, The Netherlands) is one of the most widely used programs for processing and editing 3D images [19,23]. It is available at www.blender.…”
Section: Blender and The Blenderfordental Add-onmentioning
Among the innovations that have changed modern orthodontics, the introduction of new digital technologies in daily clinical practice has had a major impact, in particular the use of 3D models of dental arches. The possibility for direct 3D capture of arches using intraoral scanners has brought many clinicians closer to the digital world. The digital revolution of orthodontic practice requires both hardware components and dedicated software for the analysis of STL models and all other files generated by the digital workflow. However, there are some negative aspects, including the need for the clinician and technicians to learn how to use new software. In this context, we can distinguish two main software types: dedicated software (i.e., developed by orthodontic companies) and open-source software. Dedicated software tend to have a much more user-friendly interface, and be easier to use and more intuitive, due to being designed and developed for a non-expert user, but very high rental or purchase costs are an issue. Therefore, younger clinicians with more extensive digital skills have begun to look with increasing interest at open-source software. The aim of the present study was to present and discuss some of the best-known open-source software for analysis of 3D models and the creation of orthodontic devices: Blue Sky Plan, MeshMixer, ViewBox, and Blender.
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