# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 4 Discussion
The device used a wireless handheld intra-oral camera to capture oral teeth image and sent it for data processing. The registration method used the real-time natural feature registration to superimpose the ideal bracket position of the virtual object on the tooth surface (Figure 7). We used the real-time corner detection algorithm to achieve real-world integrity and high registration accuracy [3]. The AR-assisted bracket navigation system was displayed on the screen in front of the operator instead of a head-mounted device to reduce the load on the operator's neck. Under acceptable error rate, the proposed system could provide more fluent user experience because there was no huge number of calculations that needed to be done within the limited time. As a result, deviations in the expert group in the vertical (incisogingival) direction were 0.40 mm and 0.29 mm for the control and AR groups, respectively, and the improvement rate was 28%. Deviations in the novice group were 0.90 mm and 0.51 mm, respectively, and the improvement rate was 43%. Notably, using the AR-assisted bracket navigation system, the accuracy of the novice orthodontist closed that of the expert orthodontist using the Boone gauge. However, no significant differences were observed in the horizontal (mesiodistal) direction regardless of the orthodontist's experience or the system used. The results showed no significant difference between the experimental group and the control group, and the possible reason is that the pre-treatment of root parallelism was not taken into consideration. However, the research is still valuable for clinical practice due to the fact that the horizontal deviations of the two methods were less than 0.5 mm. The standard of 0.5 mm is regarded as the acceptable clinical limitation according to the professional standard of the American Board of Orthodontics Objective Grading System [25]. This system recommends that the deviation of alignment and marginal ridges should be at the same level or within 0.5 mm [25]. As reported by [PERSON] et al. (2014) [26],  
Figure 7: The registration method used real-time natural feature registration to superimpose the ideal bracket position of the virtual object on the tooth surface. The real-time corner detection algorithm was used to achieve real-world integrity and high registration accuracy. who investigated the linear differences between five indirect bonding techniques, the deviation ranged between 0.06 mm and 0.49 mm [26]. According to an in vivo study by [PERSON] et al. (2015) [13], statistical differences were confirmed under the 0.5 mm deviation limitation. The acceptable deviation in the present study was consistent with those in the two aforementioned studies. Intraoral camera resolution plays a crucial role in the accuracy of bracket bonding when the AR-assisted bracket navigation system is used. The higher the resolution, the more detailed the information in the unit pixel. We proposed the feature-based facial axis of the clinical crown (FACC) detection algorithm to decrease the error in mapping the augmented information to the real image. We extracted the contour of each tooth from the color image based on the difference in the signal between the hard and soft tissue in the color space. Next, on the basis of the tooth contour, the incisor line and FACC--two crucial reference lines--were detected for each tooth using the proposed algorithm. The positions of the bracket on each tooth depended on these two reference lines. Thus, the system used indirect information to ensure the accuracy of the augmented information during mapping from the virtual world to the real world. A high accuracy in bracket positioning can reduce the need for the first- and second-order bends, thus reducing treatment time and complexity, as well as reduce the clinical chair time required for bracket rebonding and leveling, thus ensuring efficient and precise treatment.