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Professor and Honorary Consultant in Maxillofacial and Craniofacial Rehabilitation, Academic Centre of Reconstructive Science, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London, England
Designing nasal prostheses can be challenging because of the unpaired nature of the facial feature, especially in patients lacking preoperative information. Various nose model databases have been developed as a helpful starting point for the computer-aided design of nasal prostheses, but these do not appear to be readily accessible. Therefore, an open-access digital database of nose models has been generated based on a 3-dimensional (3D) morphable face model approach. This article describes the generation of the database, highlights steps for designing a nasal prosthesis, and points readers to the database for future clinical application and research.
Various computer-aided design (CAD) techniques have been used to aid facial prosthesis design, including free-form sculpting,
In these scenarios, a preexisting systematically organized digital library of varying nose shapes or proportions could provide a rapid and straightforward way of trialing multiple options as a starting point for prosthesis design.
because of missing links or because data have been incorporated in software programs, which may now be obsolete. Creating new databases of raw scans can have limitations, as identifying and scanning new volunteers can be resource-intensive, raw scans can be difficult to manipulate, and sharing personal data has important ethical considerations.
The Leeds face model (University of Leeds) was generated based on the facial scans (DI3D; Dimensional Imaging Ltd) of over 100 volunteers of varying ages, sexes, and ethnicities. The model uses 219 dimensions that describe an instance of a face. When all numbers are set to 0, the mean face is produced. Each number is scaled as the standard deviation of that dimension, with a value of 1 representing 1 standard deviation from the norm.
To create a nose model database with sufficient facial variation, random numbers were generated for the 219 dimensions at up to both 1 and 2 standard deviations to create a sample of 200 randomly generated faces.
The area of interest was marked on the Leeds face model (Fig. 1) to indicate the planned outline of the nose models with extension beyond the nasal region to facilitate blending. The randomly generated faces were cropped, and the 200 resultant noses were manually screened to remove implausible or noticeably similar examples.
The final 44 noses were summarized in a database guide with thumbnail images and written descriptions of bridge profile, tip projection, nose length-to-width ratio, and key measurements (nose length, nasal bridge length, and nose width) (Fig. 2). The open-access database of nose models was made available for research and clinical use.
The following technical steps outline the process for designing a nasal prosthesis with the database of nose models generated by using a 3DMFM approach. A facial scan of a volunteer has been used as an example, and their nose removed to mimic a nasal defect. The volunteer provided facial photographs to imitate preoperative images to aid CAD. The reader can review the figures to compare the facial prosthesis design to the original facial scan to consider the suitability of this approach.
Collect all required data. Make a facial scan with an appropriate facial scanner, such as a handheld structured light scanner (Artec Space Spider; Artec 3D). Process the data, and export the facial mesh (Figs. 3 and 4). Source clinical or patient photographs showing the preoperative nose shape.
Review the database guide to select appropriate nose model(s) to trial. Select nose shape by comparing preoperative information to thumbnail images and written descriptions. Consider the nose model measurements and remember that nose models can be resized. In patients with limited preoperative information, use neoclassical canons or geometric rules to support nose model selection but account for ethnic variation and patient's preference.
Import the facial mesh and nose model(s) into the operator’s preferred CAD software program (such as Geomagic Freeform; Oqton). Orient the facial mesh so that it is approximately aligned with the nose models (Fig. 5).
Review the nose model(s) and select the most appropriate one based on the operator’s expertise, patient’s wishes, and any available preoperative photographs.
Resize or rescale the selected nose model as required and fine-align to the facial mesh. To guide positioning of the nose model, review Figure 1 which illustrates the region that has been cropped from the randomly generated faces. The addition of a midsagittal reference plane may help identify the correct rotational positioning of the nose model.
Blend the margins of the nose model to the facial mesh with the CAD software program (Geomagic Freeform; Oqton) and sculpting tools (Fig. 6). Thicken the fit surface of the nose model to the desired thickness to ensure structural integrity of the silicone material and adapt to the facial mesh.
Modify the design to incorporate any retentive components and adjust the nasal features (such as the nasal contours or nostrils) to the needs of the patient (Fig. 7).
Once completed, use the CAD file to produce a 3D printed replica or negative mold according to the operator’s preferred digital workflow and 3D printing materials (such as Model V2 Resin; Formlabs).
The digital nose model database may support operators with an understanding of prosthetic principles and general CAD techniques to design nasal profile dressings and intermediate or definitive nasal prostheses. While no absolute contraindications exist for using this database, other CAD techniques may be more appropriate in specific clinical contexts. Preoperative scans may be better suited to situations where a patient’s nasal anatomy is not distorted and facial scanning can be coordinated before surgery.
With combined midfacial defects, the database could be used in conjunction with other techniques (such as mirroring) but could be more complex because of disruption of key landmarks and lines of symmetry.
As all nose models have a standardized orientation, operators can rapidly trial multiple options to identify a sensible starting point for CAD. The polygon file format will be compatible with a variety of software programs, and the straightforward, well-behaved meshes will be ideal for resizing and manipulating with CAD tools. The nose models will be less detailed than raw meshes, and additional features (such as folds and creases) may need to be added.
While the Leeds face model has been created based on the scans of over 100 volunteers of varying ages, sexes, and ethnicities, the sample may be insufficient to create a truly representative model that fully explains the shape variations of nasal features.
The low prevalence of some features within the database (such as concave bridges) may be attributed to fewer instances within the sample population and consequently a lower probability of being generated. The 3DMFM dimensions were varied at up to 2 standard deviations to generate a variety of nose models including less commonly encountered shapes. The number of nose models was then reduced to balance shape variety with clinical usability. The database could be refined as training set sizes are increased or demographically specific 3DMFMs are produced.
Further improvements could be implemented to semiautomate key CAD steps. Software programs could be developed to automatically select, align, and rescale nose models based on the selection of facial landmarks. Vertices corresponding to the nostrils could be deleted programmatically. Augmented reality programs could aid nose model selection and facilitate greater patient input.
Further research is underway to assess the accuracy and repeatability of using 3DMFMs to aid facial prosthesis design in laboratory and clinical settings.
The generation of a user-friendly, open-access digital database of nose models based on a 3DMFM approach is described. The CAD technique offers a sensible starting point for facial prosthesis design and may be useful where preoperative information is limited. Research is ongoing to assess the use of 3DMFMs in aiding facial prosthesis design.
Funding: Supported by the National Institute for Health and Care Research (NIHR; England), NIHR Doctoral Fellowship (NIHR300235), and by Leeds Hospitals Charity (England). Funding for Research and Innovation (ULXXO/A200515). The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care. Data statement: Data associated with this article are available from the University of Leeds Data Repository (https://doi.org/10.5518/1228).