This is a proof-of-concept study in which a digital workflow was implemented to produce a rigid and a semi-rigid 3D-printed facemask. These 3D-printed facemasks were compared with the traditional facemask typically used in burn rehabilitation. A 48-year-old female patient with a history of flame burn affecting 25% TBSA involving the face, neck, upper chest, back, and bilateral upper limb. For the facial burn injury, initial debridement and application of split thickness skin graft was done for the forehead and bilateral upper eyelid Fig. 1a. The face masks were applied during the initial rehabilitation period for scar management.
In our institution, traditional mask was fabricated by an experienced therapist using Orfilight® Atomic Blue NS (Orfit, Wijnegem, Belgium), a micro-perforated low-temperature thermoplastic splinting material. The thermoplastic sheet was activated by heating it at a temperature of 65–70 °C using a water bath. Once the material was activated, completely soft and can be hand handled after brief cooling period, it was then molded directly on the patient’s face to reach the desired shape. It was kept on the patient face till it fully cooled down and was sufficiently hardened. Padding of the edges was done using moleskin® and adjustable straps were affixed to the mask using self-adhesive material on the superior and inferior ends as shown in Fig. 1b.
For 3D printed masks, a threefold digital workflow was composed of 1) Acquisition of 3D data using smartphone-based 3D scanner to capture patient’s face, 2) 3D construction of personilized fasemask compatable with 3D printing using open-source CAD, and 3) printing the facemask using thermoplastic material on a desktop 3D printer.
Acquisition of 3D data
We have utilized a smartphone (iPhone 12, Apple®) with facial recognition capabilities to perform the 3D scanning process of the patient’s face. The scanning was done utilizing Bellus3D FaceApp (Bellus3D® Campbell, CA) with the patient seated in upright position. Bellus3D FaceApp is a smartphone application that uses the phone facial recognition senser for 3D scanning and costs 0.99 US dollars per model for the export feature as a Alias Wavefront Object (.OBJ) file. Initially, few trials of scannning were done to familiarize the patient with the process. The scanning process acquired data in two axes (while patient turning the head to the sides and then by flexing and extending the neck) that helped acquiring more surface details of the patient’s face.
Designing the facemask
The scanned model was exported in (.OBJ) format. The model was imported into Blender (Blender® Foundation, Vienna, Austria), an open-source CAD modeling software, in which sculpt mode was selected for further processing. “Mask extract” tool was then selected followed by manual highlighting of the area of interest which included the following zones: forhead, periorbital area, nose, and upper cutaneous lip as shown in Vid. 1, The resulting facemask was exported as. STL file. This partial facemask design was done to reduce the need for mask removal during meals, oral hygiene, or verbal communication. The facemask model was further edited using Meshmixer® (Autodesk Inc.), an open-source software, to give the STL file more volume and thickness to be printable. The file was then exported and was ready for 3D printing.
3D-printing and post-production manual refinements
The design was 3D printed using material extrusion on a desktop fused deposition modeling 3D printer (Ultimaker 2+, Ultimaker®, Geldermalsen, The Netherlands). Two materials were tested for mask fabrication. The rigid facemask was printed using a biodegradable polylactic acid (PLA) filament Fig. 1c. A semi-rigid mask was printed using a thermoplastic polyurethane (TPU) filament Fig. 1d.
Total time for mask fabrication and printing was recorded together with the cost per material. The print settings for the produced masks are summarized in Table 1.
After the masks were printed, the resulting models were cleaned with the removal of its additional support material. Then padding of the edges of the masks was done using moleskin® to minimize skin friction. Two adjustable straps were affixed to each mask using self-adhesive material on the upper and lower ends. The steps of conventional and the digital workflow for mask production are summarized in Fig. 2.
All masks were worn with adjunct medical grade silicone sheet as a lining underneath. Each mask was scheduled to be worn for a period of 7 days. Total number of hours wearing the mask during the day and the presence of any side effects were recorded. A daily assessment of the patient’s level of comfort was done on a numerical scale of 1–10, with 10 being most comfortable. The patient was assessed daily and all data were collected and logged in excel sheet. The patient gave written informed consent for their photos and the supplementary video to be used for publication. The dataset generated during and/or analyzed during the current study are available from the corresponding author on reasonable requests.
Data analysis was performed using Statistical Package for the Social Science (SPSS) in which analysis of variance (ANOVA) test was used to compare the continuous variables. A p value of < 0.05 was considered statistically significant.
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