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Accuracy of 3-dimensional virtual patient representation using different digital integration techniques and four
Panagiotis Ntovas1, Marta Revilla-León2, Abdul B Barmak3
1Scientific Affiliate, Department of Operative Dentistry, School of Dentistry, National and Kapodistrian University of Athens, Athens, Greece; Adjunct Faculty, Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Mass.; and External Coworker, Division of Fixed Prosthodontics and Biomaterials, University Clinics for Dental Medicine, University of Geneva, Geneva, Switzerland.
Statement Of Problem:
Three-dimensional virtual patient representation (VPR) can facilitate the integration of facial references into treatment planning; however, the influence of different integration techniques and facial scanning technologies on the accuracy of the virtual representation remains unclear.
Purpose:
The purpose of this clinical validation study was to investigate the impact of facial scanning technology and integration technique on the accuracy of virtual patient representation.
Material And Methods:
Intraoral scans were obtained from 40 participants. Four landmarks were placed on each participant's face. Facial scans were acquired using 4 different devices: 3 professional face scanners (MetiSmile; Shinning3D, Morpheus3D; Morpheus3D and Rayface 200; Rayteams) and a smartphone using a face scanning application (QloneDental, EyeaCue). For each face scanner, 2 different techniques for creating a VPR by integrating facial and intraoral scans were tested, relying solely on the teeth and integration based on an extraoral scan body. Using a metrology software program, 26 linear measurements were made between the predefined landmarks on the face and 4 reference points on the participant's teeth. The same interlandmark distances were measured manually on the actual participant with calipers to serve as control data. Data analysis included pairwise comparison tests using 2-way ANOVA (α=.05).
Results:
For the facial measurements, the mean trueness ranged from 0.69 to 4.66 mm, while precision ranged from 0.58 to 3.68 mm. For dentofacial measurements, trueness ranged from 0.79 to 5.17 mm for tooth-based registration and from 1.13 to 3.22 mm for extraoral scan body registration. Precision ranged from 0.67 to 3.59 mm for tooth-based and from 0.75 to 3.54 mm for extraoral scan body registration. No significant differences in trueness or precision were found between the professional facial scanners (P>.05). The smartphone-based facial scanner showed significantly lower trueness (3.70 µm) and precision (2.44 µm) compared with all other devices (P<.05). Regarding digital integration techniques, statistically significant difference was observed between tooth-based and ESB-based registrations for only the smartphone-based face scanner (P<.05).
Conclusions:
The type of facial scanner significantly influenced both the accuracy of the face scan itself and the accuracy of VPR. Facial scanners can achieve similar accuracy levels, regardless of the underlying technology or scanning method. The use of an extraoral scan body improved accuracy only when used with smartphone-based facial scanners. While smartphone-based scanners offer a cost-effective solution, their accuracy in VPR remains inferior to that of professional facial scanning systems.
