Accuracy of virtual facebow compared to conventional facebow: a systematic review
Clinical Snapshot
PICO Framework
| P — Population | Dentate patients undergoing fixed prosthodontic treatment requiring maxillary cast transfer to an articulator |
| I — Intervention | Virtual facebow transfer methods (digital photographs, face scans with/without scan bodies, smartphone applications, Kois glasses, CBCT, stereophotogrammetry) |
| C — Comparator | Conventional facebow transfer technique |
| O — Outcomes | Accuracy of maxillary digital model transfer to virtual articulators, including trueness and precision of transfer |
Bottom Line
This systematic review examines the accuracy of virtual facebow transfer methods compared to conventional facebow techniques in fixed prosthodontics. From 1,916 retrieved articles, only 10 met inclusion criteria, reflecting the nascent state of this evidence base. The qualitative synthesis suggests that several digital approaches — including digital photography, facial scanning, smartphone applications, CBCT, and stereophotogrammetry — demonstrate clinically acceptable accuracy and promising repeatability. However, the review is substantially limited by the absence of formal risk of bias assessment, no GRADE certainty of evidence rating, no PROSPERO registration, and no quantitative meta-analysis. The included studies are methodologically heterogeneous, spanning in vitro bench studies and clinical reports, making cross-study comparison unreliable. No definitive conclusion regarding superiority of any virtual method over conventional facebow can be drawn. For Australian prosthodontists, virtual facebow systems represent a promising adjunct to digital workflows, but adoption decisions should be guided by individual practice capability, patient case complexity, and an understanding that the supporting evidence remains preliminary. Clinicians should not abandon conventional facebow techniques based on this review alone. Well-designed, adequately powered, standardised clinical trials with defined accuracy thresholds are urgently needed before practice-changing recommendations can be made.
Key Findings
P Value: Not reported at review level
Effect Size: No pooled effect size calculable; qualitative synthesis indicates most studies show comparable accuracy between digital and conventional methods
Primary Outcome: Accuracy (trueness and precision) of virtual facebow transfer of maxillary digital models to virtual articulators compared to conventional facebow techniques
Nnt Or Sensitivity: Not applicable; no quantitative accuracy metrics (e.g., mean angular or linear deviation in degrees/mm) pooled across studies. One study reported superior trueness and precision for photography-based methods over facial scanning and conventional facebow; another demonstrated workflow-dependent variability in accuracy
Confidence Interval: Not reported; no meta-analysis performed
Clinical Application
Virtual facebow workflows are technically feasible in well-equipped prosthodontic practices but require investment in digital scanning hardware, compatible virtual articulator software, and operator training. Workflow standardisation remains a barrier to widespread adoption. The diversity of available methods (CBCT, stereophotogrammetry, smartphone apps, Kois glasses) means no single universally adopted protocol currently exists. In Australia, digital prosthodontic workflows are increasingly available in specialist and some general dental practices. The Therapeutic Goods Administration (TGA) regulates intraoral scanners and CBCT units as Class IIa–IIb medical devices; virtual articulator software may require TGA listing depending on intended use. The Australian Dental Association (ADA) and the Royal Australasian College of Dental Surgeons (RACDS) have not yet issued specific guidelines on virtual facebow protocols. Medicare and most private health fund schedules do not currently provide specific item numbers for digital articulation workflows, meaning costs are typically absorbed into existing prosthodontic item numbers or charged as out-of-pocket fees. The PBS is not directly relevant to dental device workflows. Practitioners considering adoption of virtual facebow systems should be aware that the current evidence base does not yet support superiority claims over conventional techniques. Dentate adult patients requiring fixed prosthodontic rehabilitation where maxillary cast transfer to an articulator is clinically indicated, particularly in practices with access to intraoral scanners, facial scanning technology, or CBCT
Abstract
BACKGROUND: Digital technologies are rapidly evolving and have been introduced across many fields in dentistry, particularly prosthetic and restorative dentistry. In digital treatment planning, virtual articulators are helpful in simulating complex maxillomandibular movements and analyzing occlusion. Despite these advancements, evidence-based literature standardizing the transfer of clinical data to virtual environments remains limited. The aim of this review was to explore the accuracy of various methods in transferring maxillary digital models to virtual articulators compared with conventional facebow techniques. METHODS: The research (PICO) question focused on assessing the accuracy of virtual facebow transfer compared to that of conventional facebows. An electronic search was performed in 3 databases between January 2014 to December 2025: PubMed/MEDLINE, Scopus, and Web of Science. Eligibility criteria included in vitro studies, clinical trials, case reports, clinical reports, and clinical techniques that evaluated the accuracy of virtual articulation in fixed prosthodontics for dentate patients, published in English. Studies were excluded if they were not in English, involved orthognathic surgery, focused on edentulous patients, or were published before 2014. A comprehensive search was conducted in December 2025. Two reviewers (PE, HA) independently examined the identified papers in accordance with the predetermined qualifying criteria. For each included study, publication details, study characteristics, and methodological aspects were documented. RESULTS: The total number of articles retrieved was 1,916. Of these articles, 10 met the inclusion criteria and were used for qualitative synthesis. Several techniques were reported for transferring the maxillary model to virtual articulator, such as digital photographs, face scans with or without intraoral or extraoral scan body, smartphone applications, digital photographs using Kois glasses, CBCT, and stereophotogrammetry. Most studies showed comparable accuracy of different digital methods that transfer the maxillary cast to the articulator. However, one study showed that the photography-based method demonstrated superior trueness and precision compared to both facial scanning and the conventional facebow technique. Another study found that the accuracy of positioning a maxillary digital scan relative to a 3D facial scan using a virtual facebow technique is highly dependent on the facial scanning method employed. CONCLUSIONS: Virtual facebow transfer can be recorded using techniques such as digital photographs, face scans, virtual facebow software, and smartphone scanning. Virtual facebow systems demonstrate promising repeatability and clinically acceptable transfer accuracy in selected workflows; however, due to heterogeneity among available studies, definitive conclusions regarding any improvement over conventional techniques cannot yet be drawn. Further standardized in vitro and clinical studies with large sample sizes, standardized assessments, and comparison methods are needed to draw meaningful conclusions in order to prove precedence of one method over the other. However, the use of a device to facilitate the transfer of a virtual maxillary cast to a virtual articulator has been promising.
References
- 1.Ellakany, P., Aggarwal, H., Khazaal, A., Kinderkenecht, K., & Ramtin-Zadeh, S. (2026). Accuracy of virtual facebow compared to conventional facebow: a systematic review. BMC Oral Health. https://doi.org/10.3390/prosthesis4040053
Related Research
International dental journal
Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning
2 Aug 2026
Journal of prosthodontic research
Peri-implant soft-tissue responses to tooth-colored abutment materials: A systematic review.
14 July 2026
Clinical oral investigations
Predicting occlusal force distribution from intraoral scans using deep learning: a pilot study
7 July 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service