Systematic Literature Review of Digital Three-Dimensional Superimposition Techniques to Create Virtual Dental Patients Tim Joda, Dr Med Dent, DMD, MSc1/Urs Brägger, Prof Dr Med Dent, DMD2/ German Gallucci, Dr Med Dent, DMD, PhD3 Purpose: Digital developments have led to the opportunity to compose simulated patient models based on three-dimensional (3D) skeletal, facial, and dental imaging. The aim of this systematic review is to provide an update on the current knowledge, to report on the technical progress in the field of 3D virtual patient science, and to identify further research needs to accomplish clinical translation. Materials and Methods: Searches were performed electronically (MEDLINE and OVID) and manually up to March 2014 for studies of 3D fusion imaging to create a virtual dental patient. Inclusion criteria were limited to human studies reporting on the technical protocol for superimposition of at least two different 3D data sets and medical field of interest. Results: Of the 403 titles originally retrieved, 51 abstracts and, subsequently, 21 full texts were selected for review. Of the 21 full texts, 18 studies were included in the systematic review. Most of the investigations were designed as feasibility studies. Three different types of 3D data were identified for simulation: facial skeleton, extraoral soft tissue, and dentition. A total of 112 patients were investigated in the development of 3D virtual models. Conclusion: Superimposition of data on the facial skeleton, soft tissue, and/or dentition is a feasible technique to create a virtual patient under static conditions. Three-dimensional image fusion is of interest and importance in all fields of dental medicine. Future research should focus on the real-time replication of a human head, including dynamic movements, capturing data in a single step. Int J Oral Maxillofac Implants 2015;30:330–337. doi: 10.11607/jomi.3852 Key words: computer-assisted image processing, digital dental medicine, image fusion, patient simulation, superimposition

F

or more than a century, analog and digital techniques have been developed to analyze the characteristics of the human face. With the invention of radiographic examination and the standardization of photography, two-dimensional diagnostic measurements have been integrated into the fields of orthodontic therapy and maxillofacial surgery.1,2

1 Assistant

Professor, Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Switzerland. 2Chair and Professor, Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, School of Dental Medicine, University of Bern, Switzerland. 3Interim Chair and Assistant Professor, Department of Restorative Dentistry and Biomaterials Sciences, Harvard School of Dental Medicine, Boston, Massachusetts, USA. Correspondence to: Dr Tim Joda, Assistant Professor, Division of Fixed Prosthodontics, School of Dental Medicine, University of Bern, Freiburgstr. 7, 3010 Bern, Switzerland. Fax: +41-31-632-4931. Email: [email protected] ©2015 by Quintessence Publishing Co Inc.

Recently, digital developments have led to a broader implementation of a variety of technologies in dental medicine. As a result, clinical practices and laboratory techniques are shifting to virtual-based workflows.3 This digitization process has resulted in the expansion of three-dimensional (3D) media to additional dental disciplines, such as prosthodontics and implant dentistry.4 Routine diagnostic methods include digital radiology and photography. Treatment concepts benefit from modernized workflows with computer-supported protocols.5 In technical fabrication, moreover, the entire manufacturing process can be streamlined to create high-quality dental prostheses.6 In this context, the major developments have been cone beam computed tomography (CBCT); optical scanning technologies, both intraoral and extraoral; digital laboratory modeling (DLM); as well as computer-assisted design/ computer-aided manufacture.7–9 However, the various technologies for facial and dental imaging have to be considered as singleoperation systems. Bringing together the data puzzle

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Table 1  Overview of Electronic Search Strategy Database

MEDLINE (PubMed) and OVID (Embase)

Timeline

Up to March 2014

P-I-(C)-O

Population/Topic {“Patient Simulation” [MeSH] OR “Computer Simulation” [MeSH] OR “Computerized Model” [MeSH]} OR {(virtual AND patient*) OR (digital AND patient*) OR (simulation AND patient*)} AND Intervention/Method {“Dental Digital Radiography“ [MeSH] OR “Cone Beam Computed Tomography“ [MeSH]} OR {(facial scan*) OR (extraoral scan*) OR (intraoral scan*) OR (digital impression*) OR (superimposition*) OR (image fusion*) OR (simulation*)} AND Outcome/Interest {“Feasibility Study” [MeSH] OR “Dimensional Measurement Accuracy“ [MeSH]} OR {(prosthodontics*) OR (implant dentistry*) OR (maxillofacial surgery*) OR (plastic surgery*) OR (orthodontics*) OR (education*) OR (teaching*) OR (training*)} AND Limits {Filters: English; Dental Journals; Humans}

pieces into a whole seems to be the logical continuum of this digitization trend in dental medicine: creating a 3D virtual patient. Health care providers, patients, and educators would benefit significantly from this further development in dental medicine. In the real world, the combination of the triad of diverse craniofacial tissue structures—facial skeleton (FS), extraoral soft tissue (ST), and dentition including surrounding intraoral soft tissue (DENT)—into a single entity remains complex. Not only are the anatomical structures unique in nature; in addition, the corresponding digital 3D data obtained from radiology and scanning techniques differ in their formal data structure.10 The replication of a 3D virtual patient requires the successful fusion of several imaging devices and data formats. • CT uses DICOM (Digital Imaging and Communications in Medicine), a general standard format for handling, storing, printing, and transmitting information in medicine.11 • Extraoral scanning captures facial extraoral skin, and data are usually stored as universal OBJ files (developed by Wavefront Technologies), a widely accepted geometry definition format that represents 3D color and texture information. • Intraoral scanning and DLM typically use STL files (surface tesselation language or stereolithography), which describe triangulated surface geometries of 3D objects but store data without color information. How far has virtual dentistry come? Today, no single craniofacial imaging technique is able to capture the complete triad with optimal quality in a single step, and many questions remain. Therefore, the aim of this systematic review is to provide an update on the current knowledge, to report on the technical progress in the

field of 3D virtual patient science, and to identify further research needs that will accomplish clinical translation.

MATERIALS AND METHODS Search Strategy and Study Selection

This appraisal was designed as a systematic review of the literature. Based on a modification of PICO (patient, intervention, comparison, outcome) criteria, a search strategy was developed and executed using an electronic search. A MEDLINE (PubMed) and OVID (Embase) search up to March 2014 was then performed for English-language articles in dental journals using the following search terms. Search terms were grouped into categories for “population/topic,” “intervention/method,” and “outcome/interest.” “Comparison” was omitted as no controlled trials were expected to emerge from this search. The search strategy was assembled from a combination of qualified Medical Subject Headings (MeSH terms) as well as unspecific free-text words in simple or multiple conjunctions: {“Patient Simulation” [MeSH] OR “Computer Simulation” [MeSH] OR “Computerized Model” [MeSH]} OR {(virtual AND patient*) OR (digital AND patient*) OR (simulation AND patient*)} AND {“Dental Digital Radiography“ [MeSH] OR “Cone Beam Computed Tomography“ [MeSH]} OR {(facial scan*) OR (extraoral scan*) OR (intraoral scan*) OR (digital impression*) OR (superimposition*) OR (image fusion*) OR (simulation*)} AND {“Feasibility Study” [MeSH] OR “Dimensional Measurement Accuracy“ [MeSH]} OR {(prosthodontics*) OR (implant dentistry*) OR (maxillofacial surgery*) OR (plastic surgery*) OR (orthodontics*) OR (education*) OR (teaching*) OR (training*)} AND {Filters: English; Dental Journals; Humans}. An additional manual search of the bibliographies of all full-text articles selected from the electronic search was performed. Furthermore, a manual search was The International Journal of Oral & Maxillofacial Implants 331

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First electronic search: 12,148 hits Application of limits 397 titles Discussion Exclusion of 348 titles Selection by two reviewers 51 abstracts Discussion Exclusion of 36 abstracts Selective hand search 6 full texts

Agreement 15 full texts

Hand + electronic search 21 full texts Discussion Exclusion of 3 full texts: Only one source of data Agreement 18 studies included Fig 1  Flowchart depicting the electronic and hand searches.

conducted in the following journals up to March 2014 including February 2014: American Journal of Orthodontics and Dentofacial Orthopedics, British Journal of Oral and Maxillofacial Surgery, Clinical Implant Dentistry and Related Research, Clinical Oral Implants Research, Dentomaxillofacial Radiology, European Journal of Oral Implantology, European Journal of Orthodontics, Facial and Plastic Surgery Clinics of North America, Implant Dentistry, International Journal of Oral & Maxillofacial Implants, International Journal of Oral and Maxillofacial Surgery, International Journal of Prosthodontics, Journal of Craniomaxillofacial Surgery, Journal of Oral and Maxillofacial Surgery, Journal of Oral Implantology, Journal of Prosthetic Dentistry, Journal of Prosthodontics, Oral Surgery Oral Medicine Oral Pathology and Radiology, and Orthodontics and Craniofacial Research (Table 1). This review investigated clinical case reports and case series, cohort studies, and randomized controlled trials focusing on humans retrieved by the systematic literature search just outlined. In detail, the criteria for study selection were: • Examination of at least one subject to create a 3D virtual dental patient • Information available on the imaging devices used • The use of at least two different 3D media types

• Availability of details on the superimposition techniques Titles and abstracts retrieved by this search were screened independently by two reviewers (TJ and GG) based on the defined inclusion criteria. Disagreements were resolved by discussion. Following this, the abstracts of all titles agreed upon by both investigators were obtained and screened again to determine whether the articles met the inclusion criteria. The full texts of the selected articles were then obtained. If the title and abstract did not provide sufficient information regarding the inclusion criteria, the full report was obtained. Again, disagreements were resolved by discussion. The final selection based on the inclusion criteria had a kappa score of 0.94 (Fig 1).

Data Extraction

The following information was obtained from the included publications: author(s), year of publication, number of patients examined, methodologic approach to the creation of a 3D virtual model (ie, method of superimposition), applied imaging techniques, and medical field of interest.

RESULTS Included Studies

The systematic search was completed in March 2014, and results are current as of this date. Of the 403 titles retrieved by the search, 51 abstracts and, subsequently, 21 full texts were selected for review. Three articles were excluded from the final analysis. Two were excluded because they provided data from only FS,12,13 and a third provided only ST data.14 Finally, a total of 18 papers were included for further data extraction.15–32 All included studies were designed as single-center, single-cohort clinical investigations in an institutional university environment. No randomized controlled trials were identified. Three different sets of 3D data were used for simulations of virtual patients, as noted earlier: FS, ST, and DENT. In this context, the most frequently used media types were FS (n = 15) and ST (n = 13), followed by DENT (n = 10). With respect to superimposition, surface-based (n = 13), point-based (n = 3), and voxelbased (n = 2) methods were applied. Overall, 16 studies used a technique that incorporated two data sets, and only two studies used all three 3D data formats to create a virtual dental patient model (Table 2). For analysis and description, the included studies were categorized into four subgroups according to the different data sets used for superimposition (Fig 2):

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Table 2  Information on the 18 Included Studies by Subgroups According to the 3D Data Used for Superimposition Methods No. of patients (N = 112)

Facial skeleton (n = 15)

Extraoral soft tissue (n = 13)

Dentition (n = 10)

Type of superimposition

Field of dentistry

Outcome

Joda and Gallucci (2014)15

1

CBCT

3D photography

IOS

Surface-based

Implant dentistry Prosthodontics

Feasibility and accuracy 3D static

Noguchi et al (2007)16

1

2D cephalometry

Laser scan

DLM

Point-based

Orthodontics

Feasibility 3D static

Ayoub et al (2007)17

6

CT

3D photography

—

Surface-based

Maxillofacial surgery

Feasibility 3D static

29

CBCT

3D photography

—

Surface-based

Maxillofacial surgery

Feasibility and accuracy 3D static

Kau (2011)19

2

CBCT

3D photography

—-

Surface-based

Maxillofacial surgery Orthodontics

Feasibility 3D static

Meehan et al (2003)20

1

CBCT

Laser scan

—

Voxel-based

Maxillofacial surgery

Feasibility 3D static

Naudi et al (2013)21

14

CBCT

3D photography

—

Surface-based

Maxillofacial surgery

Accuracy 3D static

Schendel et al (2013)22

23

CBCT

3D photography

—

Surface-based

Maxillofacial surgery Orthodontics

Accuracy 3D static

Swennen et al (2009)23

1

CBCT

2D photography

—

Voxel-based

Maxillofacial surgery Orthodontics

Feasibility 3D static

Xin et al (2013)24

1

CT

3D photography

—

Surface-based

Maxillofacial Surgery

Feasibility 3D static

Katase et al (2013)25

10

CBCT

—

DLM

Surface-based

Prosthodontics

Feasibility 3D static

Lee et al (2012)26

1

CBCT

—

IOS

Surface-based

Implant dentistry

Feasibility 3D static

Lin et al (2013)27

14

CBCT

—

DLM

Surface-based

Maxillofacial surgery

Accuracy 3D static

Nkenke et al (2004)28

1

CT

—

DLM

Point-based

Maxillofacial surgery Orthodontics

Accuracy 3D static

Popat et al (2010)29

1

CBCT

—

DLM

Surface-based

Maxillofacial surgery Orthodontics

Feasibility 3D static

Galantucci et al (2013)30

3

—

3D photography

DLM

Surface-based

Maxillofacial Surgery

Feasibility 3D static

Rangel et al (2008)31

1

—

3D photography

DLM

Surface-based

Orthodontics

Feasibility 3D static

Rosati et al (2010)32

11

—

3D photography

DLM

Point-based

Orthodontics

Feasibility and accuracy 3D static

Study

Jayaratne et al (2012)18

IOS = intraoral optical scanning.

• All three data sets (FS + ST + DENT) (n = 2): Joda and Gallucci15 and Noguchi et al16 • Two data sets, ie, FS + ST (n = 8): Ayoub et al,17 Jayaratne et al,18 Kau,19 Meehan et al,20 Naudi et al,21 Schendel et al,22 Swennen et al,23 Xin et al24 • Two data sets, ie, FS + DENT (n = 5): Katase et al,25 Lee et al,26 Lin et al,27 Nkenke et al,28 Popat et al29

• Two data sets, ie, ST + DENT (n = 3): Galantucci et al,30 Rangel et al,31 Rosati et al32 A total of 112 patients were investigated in the creation of 3D virtual patient models. The included studies focused on different fields of interest: maxillofacial surgery (n = 12), orthodontics (n = 8), prosthodontics The International Journal of Oral & Maxillofacial Implants 333

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Extraoral soft tissue (n = 13) Ayoub et al17 Jayaratne et al18 Kau19 Meehan et al20 Naudi et al21 Schendel et al22 Swennen et al23 Xin et al24

Galantucci et al30 Rangel et al31 Rosati et al32

Joda and Gallucci15 Noguchi et al16

Facial skeleton (n = 15)

Katase et al25 Lee et al26 Lin et al27 Nkenke et al28 Popat et al29

Dentition (n = 10)

Fig 2  Categorization of the 18 included studies according to the data set used for superimposition: extraoral soft tissue, facial skeleton, and dentition.

(n = 2), implant dentistry (n = 2), and the combined fields of maxillofacial surgery plus orthodontics (n = 5) and prosthodontics plus implant dentistry (n = 1). Eleven trials were designed as proof-of-principle studies, investigating the feasibility of the application method in general. Four studies reported on accuracy analysis as the primary outcome, whereas three studies examined both feasibility and accuracy (Table 2).

Descriptive Analysis

Facial Skeleton + Soft Tissue + Dentition. A three-file simulation technique (FS + ST + DENT) was performed in two studies; both reported on a proof of principle in a single case. Joda and Gallucci15 combined full-arch intraoral scans with CBCT and stereophotogrammetric facial images to build a 3D virtual patient. All data were combined into a unique data pool by means of a surface-based method. This application technique successfully demonstrated the feasibility of building a craniofacial virtual reality model by fusion of DICOM, OBJ, and STL files.15 Noguchi et al16 created a virtual patient model based on 2D radiographic cephalometry, both lateral and frontal, as well as laser scanning of the extraoral facial skin and dental cast situations. In this context, a point-related approach for superimposition was used in a one-case treatment approach for orthodontic surgery.16 Two-Method Approaches. Two-file image fusion of FS + ST was used in the largest number of studies (n = 8), followed by FS + DENT (n = 5) and ST + DENT (n = 3). The number of patients included in each study

varied from 1 to 29. Moreover, the studies differed in their design (case reports and clinical investigations) and focused on different outcomes, either feasibility in general and/or accuracy. Facial Skeleton + Soft Tissue. Ayoub et al17 performed a study to analyze the superimposition of stereophotogrammetry and conventional CT on surface-based registration in six patients. First, they assessed the feasibility of building a virtual human face digitally. Then, quantitative measurement of registration errors for the distance from the 3D photorealistic surface to the transformed CT skin surface was calculated within a range of ± 1.5 mm.17 Jayaratne et al18 evaluated the accuracy of superimposed 3D photos and CBCT images to assess the degree of error for 29 patients. The root mean square error of the distance differences between the registered surfaces was 0.739 mm. Kau19 presented two case reports to test the feasibility of a single-step approach that used CBCT and stereophotogrammetric image data. Based on surface matching, the superimposition could be executed at the time of FS and ST recording. Meehan et al20 demonstrated a feasibility method for interactive computer-assisted craniofacial plastic surgery with consideration of soft tissue changes. The simulation processes were based on one patient’s preoperative CT and a 3D photorealistic laser scan. Naudi et al21 investigated the accuracy of simultaneous capture of facial stereophotogrammetry and CBCT in 14 patients. Twice, 3D imaging was performed within 1 hour, and the scans were individually superimposed. Then, the absolute aver-

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age distance between surfaces was calculated; a level of accuracy of 0.4  mm was observed for this tested setup. Schendel et al22 measured the accuracy of 3D computer simulation of ST changes after orthognathic surgery in 23 cases. Patient-specific images were created based on photogrammetric facial scanning and CBCT. Absolute millimeter differences were computed for selected cephalometric skin markings between the simulated and the actual postoperative outcomes. The results showed that 83% of the compared landmarks differed by less than 0.5  mm. Swennen et al23 tested a setup of CBCT plus digitalized 2D photography to process a 3D virtual augmented model of one patient’s head for feasibility. The acquired image data were used for superimposition on a voxel basis. The medical field of interest was stated as orthodontic surgery. Xin et al24 demonstrated in a case series the feasibility of building a craniofacial virtual reality model via fusion of CT and facial stereophotogrammetry images in six patients. A genetic algorithm was used for image fusion by means of surface superimposition to produce a single file. Facial Skeleton + Dentition. Katase et al25 evaluated the feasibility of a simulation method in the field of prosthodontics. Ten edentulous patients were simulated with integrated data from CBCT and digitalized complete dentures. A surface-based method was used for superimposition of data points. Lee et al26 used DICOM data obtained from CBCT and merged it with STL data obtained from intraoral scanning. On the basis of the two superimposed data sets, a surface-related 3D patient model was created for implant planning. Lin et al27 investigated an artifact-resistant surface-based registration method that did not require individually placed markers. CBCT and laser-scanned dental cast models were used in developing the method and to examine the accuracy of the superimposition for 14 patients. The experimental results showed clinically acceptable mean accuracy errors that ranged from 0.10  to 0.43  mm. Nkenke et al28 focused on the accuracy of fusion of CT and scanning of dental casts in an experimental setting. A CT and two different optical 3D images of dental plaster casts with and without metallic artifacts in the region of the first molars were acquired from a patient. The imaging data were registered by means of point-related superimposition for both pairs of casts. Metal artifacts significantly reduced the accuracy of the fusion of CT data and optical 3D imaging. Popat et al29 presented a method that superimposed data obtained from CBCT and digital dental models for 3D orthognathic planning. This feasibility study successfully used surface-based superimposition of the 3D data files to create a virtual dental patient. Soft Tissue + Dentition. Galantucci et al30 described a methodology to scan and integrate facial ST surfaces with dental hard tissue models. The scans

were aligned to obtain virtual data sets of three patients. The data were acquired with laser scanning and photogrammetry, as well as photogrammetry only. Surface-based superimposition produced navigable 3D models that included dimensional measurements directly in the virtual space. Rangel et al31 published a technical report showing the integration of a digital dental cast with a set of 3D facial photographs. For superimposition, an iterated point algorithm was used to match the correct anatomical positions within the 3D files. The distance between the fused data sets was calculated, with an average error of 0.35 mm. Rosati et al32 digitally integrated dental models and facial morphology with a 3D stereophotogrammetric imaging system in 11 adults. Three dental as well as three facial landmarks were chosen for a point-related superimposition. Linear measurements were made between the occlusal plane and the facial landmarks to determine the level of accuracy. The greatest mean relative error was less than 1.2%.

DISCUSSION The topic of digital medicine, and especially the creation of a 3D virtual patient, is of great interest for all dental disciplines.33 The output of research projects investigating virtual technologies has been continuously increasing in recent years.34 This progress was evident in this systematic review; 61% of the included studies had a publication date within the last 3 years. The creation of a virtual patient is dependent upon the integration of 3D media files and the possibility of their fusion into a unique and replicable model. In dental medicine, an imaging triad of the FS, the extraoral ST, and DENT (including the surrounding intraoral soft tissues) is required for accurate superimposition.32 In this context, the data types can be used in four combinations for superimposition, either integrating all three data sets or two in various combinations. Based on the technical implementation, then, the 3D data can be matched using point-related, surface-related, and voxel-related fusion methods. The matching process of the first method is based on corresponding landmarks, whereas the other two use congruent surfaces or voxels of manually selected regions.35,36 Interestingly, only two case reports, each of a single patient, reported on an approach that incorporated FS, ST, and DENT. It can be concluded that this most complex superimposition technique is still in its infancy. Today, there is no single perfect concept. However, it must be emphasized that the selection of a particular technique or techniques affects the outcome of the virtually generated patient model, as does the field of interest and the clinical use of the model. Overall, The International Journal of Oral & Maxillofacial Implants 335

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the virtual patient replication technique seems to offer potential applications in various clinical scenarios. In complex interdisciplinary cases (eg, orthognathic surgery), a superimposition model that used all three data sets (FS, ST, and DENT) of the entire head would be crucial to successful treatment, because this treatment will influence the skeletal situation, including the occlusion and the facial appearance of the patient. In advanced implant prosthetic cases, a concentrated triad approach, limited to the relevant anatomical regions of the mandible and the maxilla (including the sinuses), could provide sufficient information for treatment planning. The patient would significantly benefit from the 3D model situation with its capacity to analyze anatomical structures and simulate prosthetic outcomes in advance. For example, a goal of future therapy planning should be the pretreatment evaluation of whether adequate lip support could be achieved with or without removable prostheses in demanding esthetic-functional rehabilitation protocols in edentulous or partly dentate situations. Moreover, the amount of radiation exposure could be reduced because the field of interest for digitalization would be scaled down. For most conventional prosthodontic treatment concepts, in contrast, a procedure that included ST and DENT would be adequate to simulate prosthetic treatment outcomes. Here, the patient will profit from a method that does not include ionizing radiation and is completely noninvasive, fast, and easily aligned in a 3D model. Moreover, the procedure could be repeated at any time and would allow documentation for followup records. It should be taken into account that all the currently available fusion models were investigated in university settings. More time is required to evaluate and validate the various methods before the fusion models can be routinely implemented in daily clinical practice. Moreover, valid accuracy tests have to be developed so that the different superimposition techniques can be compared based on the 3D media files. From an economic point of view, it must be considered that additional costs are necessary to implement these new applications and devices. Overall, the superimposition of 3D data sets of FS, ST, and/or DENT information to create a virtual dental patient will allow outstanding advantages in future dental medicine, including (1) simulation of treatment planning and clarification of patients’ expectations in general; (2) more effective patient and interdisciplinary communications as well as implementation in dental education; (3)  the use of noninvasive imaging techniques for high-precision anatomical documentation; and (4) a wide range of uses in prosthodontics, implant dentistry, orthodontics, dentofacial orthopedics, and

maxillofacial and plastic surgery, as well as in interdisciplinary treatment protocols. It should be emphasized that the results of the involved studies showed a high variety of heterogeneous approaches, most of which focused on the feasibility of the used techniques. Moreover, the patient numbers in the included studies varied from 1 to 29. At present, it is very difficult to assess the quality of each included study without a reproducible and validated evaluation scale. Therefore, a comparative analysis is meaningful only in a descriptive way to summarize the current knowledge and understand the technical progress in this area. However, the articles and the total number of patients are scarce, such that a metaanalysis could not be performed. All included investigations presented only 3D virtual simulations under static conditions. Dynamic actions of the jaws, lips, and muscles would be needed to build a complete 4D replication of a human head, integrating the skeleton, extraoral and intraoral soft tissues, and dentition, and have not yet been described. This would seem to be a crucial step in the progress of this technique toward a 4D virtual patient in motion. Although it is feasible to extract a single frame of 3D data from a captured 4D video sequence and export this for superimposition with CBCT data, no commercially available system is (yet) able to fuse a 4D sequence of facial movements onto DICOM, OBJ, STL, and/or any other 3D medical file format. Future investigations are necessary to validate the accuracy of the different systems. Additional images can be easily added to existing data pools to avoid duplicate imaging for the patient and for follow-up documentation. In general, an easily transferable and uniform file format for FS/ST/DENT must be developed and established for medical 3D digital media. In addition, the workflows for superimposition techniques need to be simplified. In this context, a one-step approach with one device would be a major improvement for the generation of all 3D media at the same time and could even increase the accuracy level. In addition, the next research goal must be the development of a real-time animated virtual dental patient setting that includes movement.

CONCLUSIONS Based on the results of the literature, it can be concluded that • Superimposition of data on the facial skeleton, extraoral soft tissue, and/or dentition is currently a feasible means to create a virtual dental patient under static conditions.

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• Three-dimensional image fusion will be of increasing interest and importance for preoperative clinical assessment and treatment planning, as well as postoperative follow-up documentation. • Future research should focus on the fourdimensional replication of a human head that includes dynamic movements in real-time conditions and on the capture of digital data for virtual modeling in a one-step approach to improve accuracy.

ACKNOWLEDGMENTS This systematic review was supported by a personal research grant from the Swiss National Science Foundation, Bern, Switzerland. The sponsor had no influence upon the study design, analysis, or interpretation of the data, on the writing of the manuscript, or on submission for publication.

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