Editor’s Pick: Augmented Reality Can Improve Accuracy in Identifying Botulinum Toxin Injection Sites – European Medical Journal
Among individual points, participants were only significantly more accurate with the app than the diagram in identifying sites 2 (3.4 mm versus 5.4 mm; p<0.05), 7 (6.6 mm versus 13.6 mm; p<0.05), and 8 (4.3 mm versus 8.7 mm; p<0.05). Differences in accuracy with use of the app versus the diagram did not meet statistical significance for all other points.
After sensitivity analysis accounting for 1 mm error during image analysis, accuracy remained significantly higher with use of the app versus the diagram (5.6 mm versus 6.8 mm respectively; p<0.05 [hl]Figure 2D[/hl]).
Time taken to mark all 10 injection sites on the face of the test subject was available for eight participants. Participants took significantly longer to complete the task when using the app versus the anatomy diagram (79.8 versus 57.8 seconds; p<0.01).
There was a statistically significant difference in participants’ confidence in identifying injection sites among participants before the task, using the app, and using the anatomy diagram, χ2(2)=8.970; p=<0.05. Post-hoc analysis with Wilcoxon signed-rank tests was conducted and Bonferroni correction applied. Median (interquartile range [IQR]) for confidence levels in identifying injection sites before the task, using the app, and using the anatomy diagram were 0.00 (0.00–2.25), 5.50 (3.00–8.00), and 6.00 (2.75–6.25), respectively, and significance level was set at p<0.017. Participants demonstrated a statistically significant increase in confidence levels when using the app compared with their confidence levels prior to the task (Z=-2.395; p<0.05) and a statistically significant increase in confidence levels when using the diagram compared with their confidence levels prior to the task (Z=-2.530; p<0.05). However, there was no significant difference between participants’ confidence levels using the app versus using the anatomy diagram (Z=-0.852; p=0.394).
Usefulness and perceived accuracy
There was no statistically significant difference in participants perceived usefulness of the app alone, anatomy diagram alone, nor app and anatomy diagram combined, as tools for identifying botulinum toxin injection sites χ2(2)= 1.067; p=0.587. Median (IQR) for participants’ perceived usefulness in identifying injection sites using the app, using the anatomy diagram, and both adjuncts together were 6.00 (4.75–7.25), 6.00 (4.75–6.25), and 6.00 (5.00–8.25), respectively.
There was no statistically significant difference in participants perceived accuracy using the app versus the anatomy diagram χ2(2)= 0.000; p=1.000. Median (IQR) for participants’ perceived accuracy using the app and using the anatomy diagram were 5.50 (3.00–8.00) and 5.00 (3.75–6.25), respectively.
Of the participants, 70% reported they were willing to use the app to guide them if they were to perform real botulinum toxin injections. Despite this, a number of participants stated they preferred using the diagram as an aid.
Participants reported two significant issues that caused movement of the face filter when using the app: difficulty in holding the supporting device still; and adjustment of the image frame when a participant’s hand moved into view of the camera during marking of an injection site. These frame shifts were particularly apparent when marking injection sites on the side opposite to a participant’s dominant hand and contributed to time delays in marking injection sites when using the app.
In this study, an AR face filter of facial muscles was developed by repurposing readily available software that is used recreationally to make social media face filters. This AR face filter allowed users to identify sites for facial botulinum toxin injection significantly more accurately than through traditional means of using a reference anatomy diagram. This improvement in accuracy is important in facial injection of botulinum toxin as it may minimise risk of side effects for patients. The results also demonstrate the potential of this readily available technology both as a learning tool and as a clinical aide for practitioners with limited experience.
Despite improvements in accuracy, participants were slower and no more confident in their performance when using the AR app compared with the anatomy diagram. Participants attributed this to difficulties in keeping the supporting device still, resulting in small but notable movements of the face filter. In the future, these concerns could be addressed by using hands-free devices or stands to keep the device camera stable. Specially designed head-mounted displays, or ‘smart glasses’, have already been trialled with success in neurosurgery11 and maxillofacial surgery,12 and the app-supporting headset Microsoft HoloLens (Microsoft Corporation, Redmond, Washington, USA) is being applied in multiple healthcare settings, including facilitating virtual ward rounds13 as well as AR-assisted surgery.14.15 A similar approach may therefore show benefit in botulinum toxin facial injections.
Over the previous decade, research into the use of AR within medicine has become increasingly prevalent. AR has predominantly been used to facilitate imaging-guided surgery by enabling pre-operative CT or MRI images to be overlain onto the surgical field and guide an operation in the context of the patient’s real anatomy.16 This technology has been trialled in areas of the body that are generally non-deformable as manipulation of these tissues during surgery is minimal, requiring less processing power to track anatomy and maintain accurate image overlay. Consequently, AR research has focused on maxillofacial surgery,12,17,18 neurosurgery,11,19-22 orthopaedic surgery,15,23,24 and hepatobiliary and pancreatic surgery.25-29
In contrast, this preliminary study suggests that AR has the potential to be harnessed in clinical domains without the need for prior imaging of the test subject. Here, facial recognition software designed for social media purposes was used to detect facial landmarks and allowed the filter of facial muscles to be overlayed, such that the resultant AR image forms an accurate representation of the test subject’s underlying musculature. This technology can be relied upon further as facial recognition systems are becoming increasingly more accurate. In 2020, the best face identification algorithm had an error rate of 0.08%, compared to 4.10% for the leading algorithm in 2014.30 In a recent study, a dedicated AR guide for botulinum toxin injection was developed by combining facial recognition software with a standard oral maxillofacial model based on CT or MRI images of patients.9 With this guide, a mean accuracy of 0.40±0.25 mm was demonstrated with a range of 0–3 mm, a standard the authors deemed sufficient for use in clinical practice.9
AR filters must clearly achieve a higher standard of accuracy for use in clinical practice than for recreational use. With an average accuracy of 4.6 mm, the AR app used in this study does not meet the 3.0 mm error margin proposed as the limit suitable for clinical practice.9 However, this preliminary study is the first to suggest that readily available software, designed for recreational social media purposes, can be harnessed to improve the accuracy of facial botulinum toxin injection when compared to the use of a standard anatomy diagram.
The study’s findings are limited by its small sample size. While the results provide an encouraging basis for future research into use of AR for improvement of facial injection accuracy, the study is not adequately powered to draw definitive conclusions. Further research would involve using a larger cohort of participants as well as test subjects of different ages and genders to examine the reliability of the AR app. The ‘gold standard’ reference injection sites could also be refined by averaging opinions from multiple experienced botulinum practitioners, instead of the single expert consulted in this study.
As face filters and facial recognition technology are refined for entertainment and recreational purposes on social media, it is only a matter of time before this AR technology is routinely used in medical practice. In this preliminary study, participants were more accurate using an AR face filter app, developed using popular social media software, than they were with a traditional anatomy diagram. While participants did not perceive the app to be any better than the diagram, the improved accuracy using the app demonstrates a clear benefit. It is evident that this technology opens a promising avenue for not only training purposes, but with refinement and further advances, it has the potential to improve the accuracy of facial injections and reduce rates of complication in clinical practice. Further research is needed in optimising this technology prior to trialling its use in patients; however, AR seems to be a viable and useful adjunct for procedures requiring anatomy knowledge of the facial muscles.
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