Advancements in craniofacial prosthesis fabrication: A narrative review of holistic treatment

The accuracy of three-dimensional facial scan obtained from three different 3d scanners

This study aimed to compare the accuracy (trueness and precision) and reproducibility of three 3D facial scanning systems: a laser scanner (Planmeca Proface), a dual-structured light scanner (EinScan H2), and a smartphone application (EM3D Scanner). Thirty subjects with skeletal deformities scheduled for orthognathic surgery were scanned using these systems, and the resulting 90 3D facial scans were compared with facial surfaces segmented from CBCT scans. Surface discrepancies were measured using root mean square (RMS) values across five facial aesthetic areas (cheeks, nasal, perioral, and mental units) through Geomagic Control X software. The EM3D Scanner showed significantly better trueness and precision compared to the EinScan H2, particularly for the overall face (p < 0.01). Planmeca Proface showed no significant difference from the other scanners in terms of error. The nasal and perioral regions, scanned with Planmeca Proface, achieved the highest accuracy compared to other areas, while the left cheek demonstrated the lowest accuracy. Up to 80% of the scanned areas were classified as reproducible, falling within acceptable tolerance limits. Overall, trueness values ranged from 0.70 to 0.85 mm, and precision ranged from 0.68 to 0.81 mm, with deviations of less than 1.0 mm deemed highly acceptable for clinical applications. Surface regions closer to the midline were found to have higher accuracy than those on the sides of the face. These findings highlight the potential of EM3D Scanner and Planmeca Proface for accurate and reliable facial scanning, particularly in clinical settings where minimal deviation is crucial.

Polymers in 3D printing of external maxillofacial prostheses and in their retention systems

Maxillofacial defects, arising from trauma, oncological disease or congenital abnormalities, detrimentally affect daily life. Prosthetic repair offers the aesthetic and functional reconstruction with the help of materials mimicking natural tissues. 3D polymer printing enables the design of patient-specific prostheses with high structural complexity, as well as rapid and low-cost fabrication on-demand. However, 3D printing for prosthetics is still in the early stage of development and faces various challenges for widespread use. This is because the most suitable polymers for maxillofacial restoration are soft materials that do not have the required printability, mechanical strength of the printed parts, as well as functionality. This review focuses on the challenges and opportunities of 3D printing techniques for production of polymer maxillofacial prostheses using computer-aided design and modeling software. Review discusses the widely used polymers, as well as their blends and composites, which meet the most important assessment criteria, such as the physicochemical, biological, aesthetic properties and processability in 3D printing. In addition, strategies for improving the polymer properties, such as their printability, mechanical strength, and their ability to print multimaterial and architectural structures are highlighted. The current state of the prosthetic retention system is presented with a focus on actively used polymer adhesives and the recently implemented prosthesis-supporting osseointegrated implants, with an emphasis on their creation from 3D-printed polymers. The successful prosthetics is discussed in terms of the specificity of polymer materials at the restoration site. The approaches and technological prospects are also explored through the examples of the nasal, auricle and ocular prostheses, ranging from prototypes to end-use products.

Three-dimensional analysis of reconstructed skulls using three different open-source software versus commercial software

This study aimed to compare the 3D skull models reconstructed from computed tomography (CT) images using three different open-source software with a commercial software as a reference. The commercial Mimics v17.0 software was used to reconstruct the 3D skull models from 58 subjects. Next, two open-source software, MITK Workbench 2016.11, 3D Slicer 4.8.1 and InVesalius 3.1 were used to reconstruct the 3D skull models from the same subjects. All four software went through similar steps in 3D reconstruction process. The 3D skull models from the commercial and open-source software were exported in standard tessellation language (STL) format into CloudCompare v2.8 software and superimposed for geometric analyses. Hausdorff distance (HD) analysis demonstrated the average points distance of Mimics versus MITK was 0.25 mm. Meanwhile, for Mimics versus 3D Slicer and Mimics versus InVesalius, there was almost no differences between the two superimposed 3D skull models with average points distance of 0.01 mm. Based on Dice similarity coefficient (DSC) analysis, the similarity between Mimics versus MITK, Mimics versus 3D Slicer and Mimics versus InVesalius were 94.1, 98.8 and 98.3%, respectively. In conclusion, this study confirmed that the alternative open-source software, MITK, 3D Slicer and InVesalius gave comparable results in 3D reconstruction of skull models compared to the commercial gold standard Mimics software. This open-source software could possibly be used for pre-operative planning in cranio-maxillofacial cases and for patient management in the hospitals or institutions with limited budget.

A Contemporary Review of the Role of Facial Prostheses in Complex Facial Reconstruction

BACKGROUND

Maxillofacial prostheses provide effective rehabilitation of complex facial defects as alternatives to surgical reconstruction. Although facial prostheses provide aesthetically pleasing reconstructions, multiple barriers exist that prevent their routine clinical use. The accessibility of facial prostheses is limited by the scarce supply of maxillofacial prosthodontists, significant time commitment and number of clinic appointments required of patients during prosthesis fabrication, short lifespan of prostheses, and limited outcomes data.

METHODS

A literature review was completed using PubMed and Embase databases, with search phrases including face and maxillofacial prostheses. Patient cases are included to illustrate the use of facial prostheses to reconstruct complex facial defects.

RESULTS

The clinical use of facial prostheses requires a multidisciplinary team including a reconstructive surgeon, a maxillofacial prosthodontist, and an anaplastologist, if available, to provide patients with aesthetically appropriate facial prostheses. Developing technology including computer-aided design and three-dimensional printing may improve the availability of facial prostheses by eliminating multiple steps during prosthesis fabrication, ultimately decreasing the time required to fabricate a prosthesis. In addition, enhanced materials may improve prosthesis durability. Long-term outcomes data using validated measures is needed to support the continued use of facial prostheses.

CONCLUSIONS

Facial prostheses can be used to reconstruct complex facial defects, and bone-anchored prostheses are associated with high patient satisfaction. Multiple barriers prevent prostheses from being used for facial reconstruction. New technologies to assist the design and fabrication of prostheses, and cost reduction measures, may allow their use in the appropriately selected patient.

The challenge of managing oral maxillofacial rehabilitation with quality and cost-benefit

Although orofacial cancer leads to substantial functional, esthetic, and psychosocial deficits for patients, reconstructive plastic surgeries may not be indicated for large facial defects. The high costs of prosthetic oral maxillofacial rehabilitation may hamper such treatment, which commonly involves virtual planning, craniofacial implants, and computer-aided design and computer-aided manufactured prostheses. This report shows the treatment of 2 patients with large facial defects from surgical resection of cancerous tissue who were rehabilitated with implant-supported bar-clip overdentures and facial prostheses fabricated by using low-cost straightforward methods.

Accuracy of Mobile Device-Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis

Background

The accurate assessment and acquisition of facial anatomical information significantly contributes to enhancing the reliability of treatments in dental and medical fields, and has applications in fields such as craniomaxillofacial surgery, orthodontics, prosthodontics, orthopedics, and forensic medicine. Mobile device–compatible 3D facial scanners have been reported to be an effective tool for clinical use, but the accuracy of digital facial impressions obtained with the scanners has not been explored.

Objective

We aimed to review comparisons of the accuracy of mobile device–compatible face scanners for facial digitization with that of systems for professional 3D facial scanning.

Methods

Individual search strategies were employed in PubMed (MEDLINE), Scopus, Science Direct, and Cochrane Library databases to search for articles published up to May 27, 2020. Peer-reviewed journal articles evaluating the accuracy of 3D facial models generated by mobile device–compatible face scanners were included. Cohen d effect size estimates and confidence intervals of standardized mean difference (SMD) data sets were used for meta-analysis.

Results

By automatic database searching, 3942 articles were identified, of which 11 articles were considered eligible for narrative review, with 6 studies included in the meta-analysis. Overall, the accuracy of face models obtained using mobile device–compatible face scanners was significantly lower than that of face models obtained using professional 3D facial scanners (SMD 3.96 mm, 95% CI 2.81-5.10 mm; z=6.78; P<.001). The difference between face scanning when performed on inanimate facial models was significantly higher (SMD 10.53 mm, 95% CI 6.29-14.77 mm) than that when performed on living participants (SMD 2.58 mm, 95% CI 1.70-3.47 mm, P<.001, df=12.94).

Conclusions

Overall, mobile device–compatible face scanners did not perform as well as professional scanning systems in 3D facial acquisition, but the deviations were within the clinically acceptable range of <1.5 mm. Significant differences between results when 3D facial scans were performed on inanimate facial objects and when performed on the faces of living participants were found; thus, caution should be exercised when interpreting results from studies conducted on inanimate objects.

CAD/CAM-fabricated inlay restorations: Can the resin-coating technique improve bond strength and internal adaptation?

This study assessed the effect of the resin-coating technique on the bond strength and internal adaptation of computer-aided design/computer-aided manufacturing (CAD/CAM)-fabricated inlays. Seventy-two mesio-occlusal-distal (MOD) cavities were prepared and distributed into 3 groups according to the resin-coating technique utilized: uncoated group, G-Premio Bond (1-step) group, and Clearfil SE Bond 2+Clearfil Majesty ES Flow (2-step+Flow) group. The MOD inlays were fabricated and cemented with RelyX Ultimate (RXU), G-CEM LinkForce (LinkForce), or Panavia V5 (PV5). After 5,000 thermal cycles, each specimen was sectioned and subjected to microtensile bond strength (MTBS) testing and an internal adaptation evaluation. For the uncoated group, RXU exhibited higher MTBS than LinkForce and PV5 (p<0.05). The MTBS of all 1-step and 2-step+Flow groups was greater than 30 MPa. Resin coating did not influence the MTBS of RXU, whereas resin coating increased the MTBS of LinkForce and PV5. More than 95% gap-free margins were found in all groups.

Applying deep artificial neural network approach to maxillofacial prostheses coloration

PURPOSE

Maxillofacial prosthetic rehabilitation replaces missing structures to recover the function and aesthetics relating to facial defects or injuries. Deep learning is rapidly expanding with respect to applications in medical fields. In this study, we apply the artificial neural network (ANN)-based deep learning approach to coloration support for fabricating maxillofacial prostheses.

METHODS

We compared two machine learning algorithms, ANN-based deep learning and the random forest algorithm, to determine the compounding amount of pigment. We prepared 52 silicone elastomer specimens of varying colors and measured the CIE 1976 L* a* b* color space information using a spectrophotometer on the input dataset. The output of these algorithms indicated the compounding amount of four pigments. According to the algorithms' pigment compounding predictions, we prepared the specimens for validation analysis and measured the CIE 1976 L* a* b* values. We determined the color differences between the real skin color of five research participants (22.3 ± 1.7 years) and that of the silicone elastomer specimens fabricated based on the algorithm predictions using the CIEDE00 ΔE₀₀ color system.

RESULTS

The color differences (ΔE₀₀ value) between the real skin color and silicone elastomer validation specimens were 3.45 ± 0.87 (ANN) and 5.54 ± 1.41 (random forest), which indicates that the deep ANN approach produced superior results with respect to the ΔE₀₀ value compared with the random forest algorithm.

CONCLUSIONS

These results suggest that applying deep ANN is a promising technique for the coloration of maxillofacial prostheses.