Evaluation of the 3D Augmented Reality-Guided Intraoperative Positioning of Dental Implants in Edentulous Mandibular Models.

PURPOSE

This research aimed to propose a three-dimensional (3D) augmented reality navigation method with point cloud-based image-patient registration that could merge virtual images in the real environment for dental implants using a 3D image overlay and to evaluate its feasibility.

MATERIALS AND METHODS

A total of 12 rapid prototyping mandibular models were fabricated using a 3D printing method and were divided into two groups: 3D augmented reality-guided group and traditional two-dimensional (2D) image-guided group. A point cloud-based preoperative image-to-patient registration method was introduced to replace the traditional point-to-point registration. After the registration, dental implant surgery was performed in the two model groups using an augmented reality-guided navigation method and a traditional two-dimensional image-guided navigation method. The planned and actual postoperative implant positions were compared for measuring positional implantation errors. The surgery time was also recorded and compared between the two groups.

RESULTS

In the model experiment, the root-mean-square deviation of registration was 0.54 mm, and the implant surgery results showed < 1.5-mm mean linear deviation and < 5.5-degree angular deviation. The augmented reality-guided implantation showed smaller horizontal, vertical, and angular errors in the apical areas of the central incisor and the canine region. The surgery time using the augmented reality-guided navigation method was significantly shorter than that using the two-dimensional (2D) image-guided navigation method (P < .05). Moreover, the volunteer experiment demonstrated that the preoperative 3D models in situ accurately overlaid onto the surgical site.

CONCLUSION

The proposed point cloud-based registration method can achieve excellent registration accuracy. Dental implant placement guided by the proposed 3D augmented reality navigation method showed better accuracy and applicability, as well as higher efficiency, than the traditional 2D image navigation method.