| Literature DB >> 32710202 |
Seung Min Oh1, Ju Young Kim2, Seungho Han3, Won Lee4, Il Kim5, Giwoong Hong6, Wook Oh7, Hyungjin Moon8, Changmin Seo9.
Abstract
PURPOSE: As filler procedures have increased in popularity, serious injection-related complications (e.g., blindness and stroke) have also increased in number. Proper and effective training is important for filler procedure safety; however, limitations exist in traditional training methods (i.e. anatomical illustrations and cadaver studies). We aimed to describe the development process and evaluate the usability of a virtual reality (VR)-based aesthetic filler injection training system.Entities:
Keywords: Aesthetic fillers; Complications; Intravascular injection; Training; Virtual reality
Mesh:
Substances:
Year: 2020 PMID: 32710202 PMCID: PMC7508957 DOI: 10.1007/s00266-020-01872-2
Source DB: PubMed Journal: Aesthetic Plast Surg ISSN: 0364-216X Impact factor: 2.326
Fig. 1The four components of the virtual reality (VR) hardware in the VR system are shown: the main computer (white arrow), optical motion tracking system (yellow arrow), models of the human face and syringe (yellow arrow heads), and VR headset (white arrow head)
Fig. 2The real-time optical motion capture program (Optitrack Motive 2.1) is shown
Fig. 3The actual training program (Unreal Engine 4.21) is shown
Fig. 4The Optitrack sensor is shown
Fig. 5The model of the human face is shown. The model comprised the hard bony structure and soft tissue, and was made of silicon. The passive markers are also shown (yellow arrows)
Fig. 6The model of the syringe is shown. An actual hyaluronic acid filler syringe was used and passive markers (yellow arrows) were attached to it. The filler product is from Sthepharm Co., Ltd. (Seoul, Republic of Korea)
Fig. 7The Oculus CV1 headset is shown
Fig. 8Photos of the system experience are shown
Baseline demographics of participants
| Characteristics | Participants ( |
|---|---|
| Sex | |
| Male | 60 |
| Female | 40 |
| Age group | |
| 20–35 yr | 33 |
| 35–50 yr | 45 |
| > = 50 yr | 17 |
| Specialty | |
| Aesthetic | 46 |
| Other | 53 |
| Experience | |
| < 1 mo | 16 |
| 1 mo-1 yr | 20 |
| 1–5 yr | 26 |
| > = 5 yr | 38 |
Mean SUS score according to subgroup
| Mean | Standard deviation | ||
|---|---|---|---|
| Sex | |||
| Male | 58.71 | 13.16 | 0.1595 |
| Female | 61.44 | 10.64 | |
| Age group | 0.0601 | ||
| 20–35 yr | 60.91 | 10.25 | |
| 35–50 yr | 56.33 | 10.73 | |
| > = 50 yr | 63.82 | 17.19 | |
| Specialty | 0.637 | ||
| Aesthetic | 60.22 | 11.72 | |
| Other | 59.06 | 12.54 | |
| Experience | 0.7926 | ||
| < 1 mo | 62.03 | 11.41 | |
| 1 mo-1 yr | 59.38 | 8.62 | |
| 1–5 yr | 58.17 | 14.06 | |
| > = 5 yr | 60.2 | 13.09 | |
SUS system usability score
Factors associated with a poor usability rating of the virtual reality-based filler training system
| Factors | Odds ratio | 95% confidence interval |
|---|---|---|
| Age between 35–50 years | 5.20 | 1.35–20.08 |
| Age > 50 years | 2.93 | 0.45–19.10 |
| Female | 0.84 | 0.26–2.79 |
| Aesthetic specialty | 1.29 | 0.41–4.08 |
| Experience > 5 years | 0.30 | 0.04–2.19 |
| Experience between 1–5 years | 2.35 | 0.43–12.96 |
| Experience between 1 month-1 year | 0.47 | 0.07–3.27 |