Abstract
Purpose
This study aimed to clarify the applicability of smartphone-based three-dimensional (3D) surface imaging for clinical use in oral and maxillofacial surgery, comparing two smartphone-based approaches to the gold standard.
Methods
Facial surface models (SMs) were generated for 30 volunteers (15 men, 15 women) using the Vectra M5 (Canfield Scientific, USA), the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the iPhone 14 Pro with photogrammetry. Smartphone-based SMs were superimposed onto Vectra-based SMs. Linear measurements and volumetric evaluations were performed to evaluate surface-to-surface deviation. To assess inter-observer reliability, all measurements were performed independently by a second observer. Statistical analyses included Bland–Altman analyses, the Wilcoxon signed-rank test for paired samples, and Intraclass correlation coefficients.
Results
Photogrammetry-based SMs exhibited an overall landmark-to-landmark deviation of M = 0.8 mm (SD = ± 0.58 mm, n = 450), while TrueDepth-based SMs displayed a deviation of M = 1.1 mm (SD = ± 0.72 mm, n = 450). The mean volumetric difference for photogrammetry-based SMs was M = 1.8 cc (SD = ± 2.12 cc, n = 90), and M = 3.1 cc (SD = ± 2.64 cc, n = 90) for TrueDepth-based SMs. When comparing the two approaches, most landmark-to-landmark measurements demonstrated 95% Bland–Altman limits of agreement (LoA) of ≤ 2 mm. Volumetric measurements revealed LoA > 2 cc. Photogrammetry-based measurements demonstrated higher inter-observer reliability for overall landmark-to-landmark deviation.
Conclusion
Both approaches for smartphone-based 3D surface imaging exhibit potential in capturing the face. Photogrammetry-based SMs demonstrated superior alignment and volumetric accuracy with Vectra-based SMs than TrueDepth-based SMs.
Keywords: Three-dimensional Surface Imaging, Smartphone-based Surface Imaging, TrueDepth, Stereophotogrammetry, Oral and Maxillofacial Surgery
Introduction
Three-dimensional (3D) surface imaging is widely employed in oral and maxillofacial surgery (OMFS), in which precise assessments of anatomically complex structures and subtle volumetric changes are critical [1–3]. The technology is utilized in numerous clinical contexts, improving patient care and communication between patients and clinicians in pre- and postoperative settings [4–11]. Therefore, 3D surface imaging has become a leading technology, gradually replacing conventional photography in surgical planning and outcome evaluations [12].
Recently, smartphone-based approaches for 3D surface imaging have been introduced [13–21]. Despite these technological advancements, smartphone-based 3D surface imaging is still not sufficiently integrated into standard procedures in OMFS.
Few studies have evaluated the capability of smartphones to capture anatomically complex facial regions. Interestingly, some studies highlight the potential of smartphone-based methodologies for capturing facial features with cost-effectiveness and portability, while other studies report limited clinical applicability. D’Ettorre et al. evaluated facial surface models (SMs) of 40 individuals, utilizing three different systems: the 3dMDtrio stereophotogrammetry system (3dMD Inc., USA), the iPhone XS with the TrueDepth-based Bellus3D Face application (Bellus3D Inc., USA), and the iPhone XS with the application Capture (Standard Cyborg Inc., USA). The research documented the duration of image acquisition and processing, and also gauged the surface-to-surface deviation and distance between 18 landmarks on the reference images from 3dMD and those obtained with Bellus3D or Capture [13]. The authors concluded that the use of smartphone applications in conjunction with the TrueDepth camera demonstrates promising results. According to the authors, the primary benefits lie in cost-effectiveness and portability. Andrews et al. compared SMs of the face captured with the 3dMDface system and the iPhone 11 Pro TrueDepth camera combined with the Bellus3D Face application. They found that 97% of landmarks were within 2 mm of error compared to the reference data. The authors reported an overall root mean square (RMS) difference between the iPhone 11 Pro and 3dMD system of 0.86 mm ± 0.31 mm. High intra-observer and inter-observer reliabilities were reported [16]. Seifert et al. performed a study involving 15 patients to compare the accuracy of three 3D facial scanning applications for the iPhone 14Pro (EM3D (Brawny Lads SoftwareUSA), Polycam (Polycam Inc., USA), and ScandyPro (Scandy LLC., USA)) with a stationary photogrammetry system (3dMD). They found that the smartphone applications demonstrated mean surface deviations of 1.46 mm for EM3D, 1.66 mm for Polycam, and 1.61 mm for ScandyPro. A mean landmark-to-landmark deviation of 1.27 mm for Polycam, 1.26 mm for ScandyPro, and 1.45 mm for EM3D was observed. The authors concluded that smartphone-based systems offer a cost-effective and portable alternative to stationary systems, particularly in resource-limited settings [20]. Nightingale et al. compared facial SMs of 20 participants acquired using the Apple iPhone 8S (Apple Inc., USA) in conjunction with the Camera + 2 iOS application (tap tap tap LLC., USA) and the Artec Spider (Artec Group, Luxembourg) structured light scanner. They found an accuracy of 1.3 mm ± 0.3 mm between Artec-based and smartphone-based SMs. They concluded that smartphone-based photogrammetry is a reliable, low-cost alternative for clinical 3D facial imaging [21]. In contrast, Thurzo et al. observed differences between SMs exceeding 3 mm when comparing SMs generated by the TrueDepth camera utilizing the Bellus3D Dental Pro application and SMs generated by cone beam computed tomography [17]. They concluded that smartphone-based 3D surface imaging has limited clinical relevance. Nevertheless, they recommended that employing smartphone-based 3D surface imaging for facial assessments, especially under circumstances where precision below 3 mm is not imperative, could still yield value.
While the studies present a thorough approach, it is crucial to note that due to technological advancement, the applicability of smartphone-based 3D surface imaging may have improved. Additionally, detailed volumetric assessments of the face were performed by few prior investigations.
To address this obstacle, this study was aimed at clarifying the applicability of smartphone-based 3D surface imaging for clinical use in OMFS by comparing two smartphone-based approaches with the established gold standard, the Vectra M5 system (Canfield Scientific, USA). SMs generated by the two approaches were subsequently compared based on their alignment with the Vectra M5 system employing landmark-to-landmark distance analyses and volumetric assessments. This comparative analysis aims to provide insights into the potential clinical use of smartphone-based 3D surface imaging and contributes to a broader understanding of its accuracy in comparison to established technologies in the field of OMFS.
Material and methods
Study protocol
This prospective monocentric study was conducted at the Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Germany, following approval from the local ethics committee (23–3400-101). The investigation involved 30 healthy adult students enrolled at the University of Regensburg, excluding individuals with recent craniofacial surgery, maxillofacial trauma, or significant skeletal deformities.
Participant preparation
Consistent with prior studies on 3D surface imaging, participants were positioned in a standardized posture under controlled lighting conditions. After receiving an explanation of the procedure, they were seated on a stool and instructed to maintain a neutral facial expression while keeping their heads in a natural position. Participants were also directed to wear a hairband and to remove any makeup. Following the protocol by Othman et al., 15 specific landmarks were identified on each participant’s face using a white eyeliner [22].
Figure 1 provides an overview of all landmarks.
Fig. 1.
Landmarks: Appearance of a 20-year-old female participant; the iPhone 14 Pro using the “3D-Scanner App” V2.1.2 was utilized to create the SM; (1) soft tissue nasion (N), (2) pronasale (PRN), (3) subnasale (SN), (4) labrale superius (LS), (5) stomion (STO), (6) labrale inferius (LI), (7) soft tissue gnathion (GN), (8) alare (AL) (L), (9) alare (AL) (R), (10) subalare (SBAL) (L), (11) subalare (SBAL) (R), (12) christa philtri (CPH) (L), (13) christa philtri (CPH) (R), (14) cheilion (CH) (L), (15) cheilion (CH) (R)
3D data acquisition
The study design included obtaining 3D SMs of each participant’s face using three different methods: stereophotogrammetry with the Vectra M5, the smartphone application “3D-Scanner App” V2.1.2 (Laan Consulting Corp., USA) utilizing the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the light detection and ranging (LiDAR) camera of an iPhone 14 Pro in conjunction with photogrammetry. The smartphone application was selected, based on its capability to offer both a “TrueDepth-Mode” and a “Photo-Mode”.
While the TrueDepth camera employs vertical-cavity surface-emitting laser (VCSEL) technology to directly generate a metric point cloud, which is later used for SM generation, in the photogrammetry setup, the iPhone’s LiDAR sensor is utilized to ensure a metrical 3D reconstruction. LiDAR employs time-of-flight measurements to ascertain the distance (i.e., depth) between an object and the sensor [23].
The stereophotogrammetry-based Vectra M5, renowned for its high accuracy and widely employed in 3D facial imaging, was utilized as a reference in the study [22, 24–26]. Organizing multiple photographs into stereo pairs and integrating their overlapping regions to create a 3D SM, stereophotogrammetry is considered the gold standard for 3D surface imaging [19, 27]. The Vectra M5 uses the Vectra Analysis Module (VAM) for SM analysis [28].
All scans were performed in a designated 3D scanning room designed for children with craniofacial deformities and orthognathic surgery patients. A comparative visualization of 3D imaging techniques is shown in Fig. 2.
Fig. 2.
Comparative visualization of 3D imaging techniques: SMs generated using three methods: (1) Vectra M5, featuring five camera pods mounted on a rigid frame for stereophotogrammetry, (2) iPhone 14 Pro “3D Scanner App” V2.1.2 in “Photo-Mode”, which required capturing images in a circular motion, and (3) iPhone 14 Pro “3D Scanner App” V2.1.2 in “TrueDepth-Mode” utilizing the device’s front-facing TrueDepth camera in a circular motion. MS PowerPoint was used to create the illustration
3D Data processing
3D data obtained from smartphone-based methods and the Vectra M5 were exported as Wavefront OBJ files. CloudCompare's (http://cloudcompare.org/) ICP implementation was employed for rough alignment, which also included the estimation of an isotropic scaling factor. Facial areas of interest (FAOI) were extracted from both the smartphone-based SMs and Vectra-based SMs, which entailed cutting the SMs at the visible face edges. These extracted FAOIs were used for alignment, ensuring that non-facial regions and noise were excluded from the alignment process. This approach minimized the potential for errors arising from non-facial regions. Subsequently, the superimposed FAOI from the smartphone-based approaches were imported into the VAM. The VAM was utilized for precise alignment, which entailed aligning the smartphone-based FAOI with the SMs generated by the Vectra M5.
Figure 3 juxtaposes a SM generated by the Vectra M5 to the smartphone-based SMs. Figure 4 provides an example of the superimposed SMs used for analysis. A flowchart summarizing the study’s methodology is presented in Fig. 5.
Fig. 3.
Facial areas of interest (FAOI): Appearance of a 20-year-old female participant; the iPhone 14 Pro using the application “3D Scanner App” V2.1.2 was utilized to create the SMs on the left and right side; SM generated using the “Photo-Mode” (left), Vectra M5 (middle); SM generated using the TrueDepth-Mode (right). SMs with applied texture above and without texture below
Fig. 4.
Presentation of the superimposed SMs: A SM generated by an iPhone 14 Pro using the “3D Scanner App’s” V2.1.2 “Photo-Mode” was superimposed onto a SM generated by the Vectra M5 (left). A SM generated by an iPhone 14 Pro using the “3D Scanner App’s” “TrueDepth-Mode” was superimposed onto a SM generated by the Vectra M5 (right)
Fig. 5.
Flowchart summarizing the study’s methodology: 3D data acquisition, 3D data processing, and measurements. The study involved SM generation using three methods: (1) the iPhone 14 Pro’s “3D-Scanner App” V2.1.2 in “Photo-Mode” (2) stereophotogrammetry with the Vectra M5, and (3) the iPhone 14 Pro’s “3D-Scanner App” V2.1.2 in “TrueDepth-Mode”. Data processing steps included extraction of facial areas of interest (FAOIs), scaling and rough alignment via CloudCompare’s ICP-Algorithm, and superimposition and precise alignment via Vectra Analysis Module (VAM) for SM comparison. Measurements included landmark-to-landmark distances and volumetric analyses for the upper face, mid-, and lower face regions utilizing the VAM
SM-comparison
For SM comparison, the software VAM was utilized. For each participant, the study compared the TrueDepth-camera-based “3D-Scanner App” SMs with Vectra M5-based SMs and the photogrammetry-based “3D-Scanner App” SMs with Vectra M5-based SMs. FAOI derived from smartphone-based SMs were compared with Vectra-based SMs, using landmark-to-landmark distance analyses and volumetric analyses.
Landmark-to-landmark distance analyses involved assessing the surface-to-surface deviation by measuring 15 distinct landmark-to-landmark distances between the superimposed SMs. The landmarks’ midpoints were selected manually using the VAM. Subsequently, the linear distances between the superimposed models were calculated.
Volumetric analyses were conducted by generating a difference model between the superimposed SMs. This process entailed analyzing the differences in volume between the upper face, mid-face, and lower face. The upper face was defined as the area from the upper hairline to an axial plane through the nasion. The mid-face was characterized as between an axial plane through the nasion and the subnasale. The lower face was defined as between an axial plane through the subnasale and the anatomical boundaries of the lower jaw. Areas were selected manually using the VAM. Subsequently, the volumetric differences between the superimposed models were calculated for each region.
To assess inter-observer reliability, a second observer independently scaled and aligned all SMs, selected all landmarks manually, and performed all volumetric and landmark-to-landmark measurements.
Figure 6 provides an example of a landmark-to-landmark-distance measurement. Figure 7 shows volumetric measurements of the upper-, mid-, and lower face. Table 1 provides an overview of all measurements.
Fig. 6.
Presentation of the landmark-to-landmark measurements: A SM generated by an iPhone 14 Pro using the “3D Scanner App’s” V2.1.2 “Photo-Mode” was superimposed onto a SM generated by the Vectra M5; measurement (1) (Nasion – Nasion) was performed. Values in millimeters (mm)
Fig. 7.
Volumetric measurements: A SM generated by an iPhone 14 Pro using the “3D Scanner App’s” V2.1.2 “TrueDepth-Mode” was superimposed onto a SM generated by the Vectra M5; Volumetric assessments of the upper face, mid-face and lower face (left to right) were performed. Values in cubic centimeters (cc)
Table 1.
Measurements performed between superimposed SMs: All measurements were performed between TrueDepth- and photogrammetry-based SMs superimposed onto Vectra-based SMs; (1) – (16) landmark-to-landmark distances; (17) – (20) volumetric distances
| Measurements | |
|---|---|
| Variables | |
| Landmark-to-landmark deviation | Volumetric deviation |
| Distance | Distance |
| (1) N – N | (17) Upper face |
| (2) PRN – PRN | (18) Mid-face |
| (3) SN – SN | (19) Lower face |
| (4) LS – LS | (20) Overall accuracy (17) – (19) |
| (5) STO – STO | |
| (6) LI – LI | |
| (7) GN – GN | |
| (8) AL – AL (L) | |
| (9) AL – AL (R) | |
| (10) SBAL – SBAL (L) | |
| (11) SBAL – SBAL (R) | |
| (12) CPH – CPH (L) | |
| (13) CPH – CPH (R) | |
| (14) CH – CH (L) | |
| (15) CH – CH (R) | |
|
(16) Overall accuracy (1) – (15) |
|
Statistical analysis
IBM SPSS 29 (SPSS Inc., USA) was used for statistical analysis. A Shapiro–Wilk test indicated that normality could not be assumed for measurements (1) to (20). When assessing the accuracy of SMs obtained from TrueDepth- and photogrammetry-based SMs in comparison to Vectra M5-based SMs, values were considered clinically acceptable if mean values did not surpass 2 mm for landmark-to-landmark distances and 2 cc for volumetric differences. This threshold was chosen in accordance with the criteria outlined by Aung et al., who characterized measurements surpassing > 2 units from reference data as unreliable [29]. A Wilcoxon signed-rank test for paired samples was employed to compare the central tendencies between the methods. The consistency between surface-to-surface and volumetric deviation was evaluated using Bland–Altman analyses. A 95% limit of agreement (LoA) of ≤ 2 mm between TrueDepth- and photogrammetry-based SMs was defined as clinically acceptable for landmark-to-landmark distances to compare TrueDepth- and photogrammetry-based SMs based on their alignment with the Vectra M5. For volumetric deviations, a 95% Bland–Altman LoA of ≤ 2 cc was defined as clinically acceptable to compare TrueDepth- and photogrammetry-based SMs based on their alignment with the Vectra M5. Inter-observer reliability was evaluated using the Intraclass Correlation Coefficient (ICC), the Wilcoxon signed-rank test for paired samples, and Bland–Altman analyses. The ICC was evaluated according to Cicchetti et al. using the following guidelines for interpretation: less than 0.40 – poor, between 0.40 and 0.59 – fair, between 0.60 and 0.74 – good, and between 0.75 and 1.00 – excellent [30]. A 95% Bland–Altman LoA of ≤ 2 cc was considered clinically acceptable for evaluating inter-observer reliability.
Results
Patient demographics
The cohort included 15 men and 15 women. Their mean age was M = 24 years (SD = ± 2.3), mean height M = 176 cm (SD = ± 8 cm), mean weight M = 69.6 kg (SD = ± 14.0 kg), and mean BMI M = 22.5 (SD = ± 3.6).
Landmark-to-Landmark Distance Analyses
Comparison of vectra M5- and smartphone-based SMs
Table 2 presents the outcomes of the landmark-to-landmark distance analyses.
Table 2.
Descriptive Statistics: Landmark-to-landmark distance analyses; comparison of Vectra M5- and smartphone-based SMs; values in millimeters (mm) for TrueDepth-based and photogrammetry-based measurements (1) – (16); IBM SPSS 29 was used for data analysis
| Descriptive Statistics | |||||
|---|---|---|---|---|---|
| Distance (mm) | |||||
| Variables | Vectra M5 – Photogrammetry | Vectra M5 – TrueDepth | |||
| Distance | N | Average | SD | Average | SD |
| (1) N – N | 30 | .65 | .63 | .9 | .75 |
| (2) PRN – PRN | 30 | .55 | .32 | 1.3 | .66 |
| (3) SN – SN | 30 | .60 | .28 | .7 | .41 |
| (4) LS – LS | 30 | .91 | .55 | .9 | .68 |
| (5) STO – STO | 30 | .85 | .65 | 1.2 | .65 |
| (6) LI – LI | 30 | .70 | .39 | .8 | .49 |
| (7) GN – GN | 30 | .87 | .49 | 1.2 | .81 |
| (8) AL – AL (L) | 30 | .64 | .35 | 1.5 | .76 |
| (9) AL – AL (R) | 30 | .88 | .64 | 1.5 | .64 |
| (10) SBAL – SBAL (L) | 30 | .73 | .46 | 1.0 | .57 |
| (11) SBAL – SBAL (R) | 30 | .78 | .49 | 1.2 | .67 |
| (12) CPH – CPH (L) | 30 | .75 | .60 | .9 | .56 |
| (13) CPH – CPH (R) | 30 | .77 | .45 | 1.0 | .64 |
| (14) CH – CH (L) | 30 | 1.32 | 1.02 | 1.5 | .95 |
| (15) CH – CH (R) | 30 | 1.02 | .64 | 1.2 | .84 |
|
(16) Overall accuracy (1) – (15) |
450 | .8 | .58 | 1.1 | .72 |
The mean value for all landmark-to-landmark distances (16) of photogrammetry-based SMs to Vectra-based SMs was calculated at M = 0.8 mm (SD = ± 0.58 mm, n = 450; Table 2). The highest deviation was found in measurement (14) (left cheilion to left cheilion) M = 1.32 mm (SD = ± 1.02 mm, n = 30; Table 2).
The mean value for all landmark-to-landmark distances (16) between TrueDepth-based SMs and Vectra-based SMs was calculated at M = 1.1 mm (SD = ± 0.72 mm, n = 450; Table 2). The highest deviation was found in measurement (14) (left cheilion to left cheilion) M = 1.5 mm (SD = ± 0.95 mm, n = 30; Table 2).
All landmark-to-landmark measurements (1) – (16) remained within a clinically acceptable range, exhibiting an overall landmark-to-landmark deviation of ≤ 2 mm, when comparing both TrueDepth- and photogrammetry-based SMs with Vectra-based SMs (Table 2).
Comparison of truedepth- and photogrammetry-based SMs
Table 3 presents the outcomes of landmark-to-landmark distance analyses, when comparing TrueDepth- with photogrammetry-based SMs based on their alignment with Vectra-based SMs.
Table 3.
Landmark–to–landmark distances: 95% Bland–Altman LoA for measurement (1) – (16); and Wilcoxon signed-rank test for paired samples for measurements (1) – (16); values in millimeters (mm); IBM SPSS 29 was used for data analysis
| Bland–Altman analysis and Wilcoxon signed-rank test for paired samples | |||||||
|---|---|---|---|---|---|---|---|
| Variables | Bland–Altman | Wilcoxon signed-rank test for paired samples | |||||
| 95% Confidence Interval | Median | ||||||
| N | Mean bias | Upper Bound | Lower Bound | Vectra M5 – Photogrammetry | Vectra M5 –TrueDepth | Asymp. Sig. (2-tailed) | |
| (1) N – N | 30 | -.25 | 1.77 | −2.26 | .43 | .64 | .14 |
| (2) PRN – PRN | 30 | -.8 | 0.51 | −2.11 | .45 | 1.27 | < .001 |
| (3) SN – SN | 30 | -.11 | .9 | −1.13 | .52 | .6 | .35 |
| (4) LS – LS | 30 | -.03 | 1.63 | −1.7 | .83 | .79 | .67 |
| (5) STO – STO | 30 | -.35 | 1.30 | −2.0 | .68 | 1.16 | .04 |
| (6) LI – LI | 30 | -.09 | 1.13 | −1.32 | .59 | .65 | .40 |
| (7) GN – GN | 30 | -.38 | 1.2 | −1.95 | .80 | 1.02 | .03 |
| (8) AL – AL (L) | 30 | −0.89 | .84 | −2.62 | .61 | 1.47 | < .001 |
| (9) AL – AL (R) | 30 | -.64 | 1.13 | −2.41 | .76 | 1.47 | .002 |
| (10) SBAL – SBAL (L) | 30 | -.25 | 1.32 | −1.82 | .63 | .82 | .08 |
| (11) SBAL – SBAL (R) | 30 | -.4 | 1.27 | −2.06 | .74 | 1.14 | .015 |
| (12) CPH – CPH (L) | 30 | -.17 | 1.36 | −1.69 | .56 | .87 | .09 |
| (13) CPH – CPH (R) | 30 | -.23 | 1.12 | −1.59 | .62 | .91 | .24 |
| (14) CH – CH (L) | 30 | -.16 | 1.89 | −2.21 | 1.05 | 1.15 | .28 |
| (15) CH – CH (R) | 30 | -.16 | 1.73 | −2.04 | .86 | 1.05 | .50 |
| (16) Overall (1) – (15) | 450 | −0.33 | 1.35 | −2.00 | .66 | .98 | < .001 |
Seven out of 16 measurements exceeded the clinically acceptable 95% Bland–Altman LoA of ≤ 2 mm. However, when contrasting the mean landmark-to-landmark deviation across all distances (16) of TrueDepth- and photogrammetry-based SMs, based on their alignment with Vectra-based SMs, the results indicate a clinically acceptable 95% Bland–Altman LoA of 1.35 mm to −2.0 mm (Table 3). The Wilcoxon signed-rank test for paired samples indicated that the deviation across all landmark-to-landmark distances (16) of photogrammetry-based measurements (median = 0.66 mm) was significantly lower than for TrueDepth-based measurements (median = 0.98 mm; Wilcoxon signed-rank test for paired samples; p = < 0.001, n = 450; Table 3). Figure 8 shows the Bland–Altman plots for the landmark-to-landmark measurements (1) – (16).
Fig. 8.
Bland–Altman Plots: Comparison of TrueDepth- and Photogrammetry-based SMs. Measurements (1) – (20); Values in millimeters (mm) for measurement (1) – (16) and cubic centimeters (cc) for measurements (17) – (20). MS Excel was used to create the illustration
Volumetric analyses
Comparison of vectra M5- and smartphone-based SMs
Table 4 presents the outcomes of the volumetric deviation analyses.
Table 4.
Descriptive Statistics: Volumetric difference between superimposed SMs; comparison of Vectra M5- and smartphone-based SMs; values in cubic centimeters (cc) for TrueDepth-based and photogrammetry-based measurements (17) – (20); IBM SPSS 29 was used for data analysis
| Descriptive Statistics | |||||
|---|---|---|---|---|---|
| Volumetric difference (cc) | |||||
| Variables | Vectra M5 – Photogrammetry | Vectra M5 – TrueDepth | |||
| Volumetric distance | N | Average | SD | Average | SD |
| (17) Upper face | 30 | 1.60 | 1.91 | 3.0 | 2.47 |
| (18) Mid-face | 30 | 2.16 | 2.34 | 4.7 | 2.86 |
| (19) Lower face | 30 | 1.53 | 2.11 | 1.6 | 1.40 |
| (20) Overall (Upper face, lower face, Midface) | 90 | 1.8 | 2.12 | 3.1 | 2.64 |
The mean volumetric difference across all volumetric measurements (20) comparing photogrammetry-based SMs to Vectra-based SMs was calculated at M = 1.8 cc (SD = ± 2.12 cc, n = 90). The highest deviation occurred in measurement (18) (mid-face) with M = 2.16 cc (SD = ± 2.34 cc, n = 30; Table 4). All photogrammetry-based volumetric differences except measurement (18) (midface) remained within a clinically acceptable range, exhibiting a volumetric difference of ≤ 2 cc, when comparing photogrammetry-based SMs with Vectra-based SMs.
The mean volumetric difference across all volumetric measurements (20) for TrueDepth-based SMs compared to Vectra-based SMs was calculated at M = 3.1 cc (SD = ± 2.64 cc, n = 90). The highest deviation was observed in measurement (18) (mid-face) with M = 4.7 cc (SD = ± 2.86 cc, n = 30; Table 4). TrueDepth-based volumetric differences exceeded the clinically acceptable range for the overall accuracy (20), the upper- (17) and mid-face (18), exhibiting an average volumetric deviation of > 2 cc, when comparing TrueDepth-based SMs with Vectra-based SMs. However, values for the lower face (19) remained within the clinically acceptable volumetric difference of ≤ 2 cc, when comparing TrueDepth-based SMs with Vectra-based SMs (Table 4).
Comparison of TrueDepth- and Photogrammetry-based SMs
Table 5 presents the outcomes of volumetric deviation analyses, when comparing TrueDepth- with photogrammetry-based SMs based on their alignment with Vectra-based SMs.
Table 5.
Volumetric measurements: 95% Bland–Altman LoA for measurements (17) – (20); and Wilcoxon signed-rank test for paired samples for measurements (17) – (20); values in cubic centimeters (cc); IBM SPSS 29 was used for data analysis
| Bland–Altman analysis and Wilcoxon signed-rank test for paired samples | |||||||
|---|---|---|---|---|---|---|---|
| Variables | Bland–Altman | Wilcoxon signed-rank test for paired samples | |||||
| 95% Confidence Interval | Median | ||||||
| N | Mean bias | Upper Bound | Lower Bound | Vectra M5 – Photogrammetry | Vectra M5 –TrueDepth | Asymp. Sig. (2-tailed) | |
| (17) Upper face | 30 | -.1.4 | 3.85 | −6.66 | 1.22 | 2.18 | .01 |
| (18) Mid-face | 30 | -.2.5 | 4.73 | −9.81 | 1.54 | 4.42 | < .001 |
| (19) Lower face | 30 | -.03 | 5.05 | −5.11 | .74 | 1.23 | .73 |
| (20) Overall (Upper face, Midface, lower face) | 90 | −1.33 | 4.9 | −7.6 | 1.14 | 2.12 | < .001 |
All volumetric measurements exceeded the ≤ 2 cc 95% Bland–Altman LoA, with the highest deviation identified in the mid-face, ranging from 4.73 cc to −9.81 cc (Table 5). The Wilcoxon signed-rank test for paired samples revealed a significant difference in volumetric distances in the upper face and mid-face between the two approaches (Table 5). When contrasting the volumetric differences across all regions (20) of TrueDepth- and photogrammetry-based SMs based on their alignment with Vectra-based SMs, the results indicated a clinically unacceptable 95% Bland–Altman LoA of 4.9 cc to −7.6 cc (> 2 cc) (Table 5). The Wilcoxon signed-rank test for paired samples indicated that the deviation across all volumetric distances (20) of photogrammetry-based measurements (median = 1.14 cc) was significantly lower than for TrueDepth-based measurements (median = 2.12 cc) (Wilcoxon signed-rank test for paired samples; p = < 0.001, n = 90; Table 5).
Figure 8 shows the Bland–Altman plots for the volumetric distances (17) – (20).
Inter-Observer Reliability
Photogrammetry-based measurements
Table 6 presents the inter-observer reliability of photogrammetry-based measurements. All photogrammetry-based landmark-to-landmark measurements demonstrated good to excellent correlation, with ICC values ranging from 0.70 to 0.97. Landmark-to-landmark measurements showed a clinically acceptable 95% Bland Altman LoA of ≤ 2 mm. The Wilcoxon signed-rank test revealed no statistically significant differences between the two observers for measurements (1) – (16) (Table 6).
Table 6.
Inter-observer reliability of photogrammetry-based measurements: Bland–Altman analysis, Wilcoxon signed-rank test and Intraclass Correlation Coefficient (ICC). Median values and mean bias in millimeters (mm) for measurements (1) – (16) and in cubic centimeters (cc) for measurements (17) – (20). OP 1 = Observer 1, OP 2 = Observer 2; IBM SPSS 29 was used for data analysis
| Bland–Altman analysis, Wilcoxon signed-rank test for paired samples and ICC | ICC | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Variables | Bland–Altman | Wilcoxon signed-rank test for paired samples | |||||||
| 95% Confidence Interval | Median | ||||||||
| N | Mean bias | Upper Bound | Lower Bound | OP 1 | OP 2 | Asymp. Sig. (2-tailed) | |||
| (1) N – N | 30 | -.08 | .38 | -.54 | .43 | .55 | .08 | .97 | |
| (2) PRN – PRN | 30 | -.03 | .42 | -.49 | .45 | .49 | .96 | .84 | |
| (3) SN – SN | 30 | .04 | .58 | -.5 | .52 | .50 | .54 | .76 | |
| (4) LS – LS | 30 | -.02 | .56 | -.59 | .83 | .85 | .39 | .92 | |
| (5) STO – STO | 30 | .11 | .88 | -.67 | .68 | .60 | .56 | .87 | |
| (6) LI – LI | 30 | -.004 | .47 | -.48 | .59 | .60 | .79 | .9 | |
| (7) GN – GN | 30 | -.005 | .53 | -.54 | .80 | .65 | .62 | .94 | |
| (8) AL – AL (L) | 30 | -.07 | .49 | -.63 | .61 | .65 | .14 | .84 | |
| (9) AL – AL (R) | 30 | .06 | .97 | -.85 | .76 | .70 | .94 | .8 | |
| (10) SBAL – SBAL (L) | 30 | -.01 | .52 | -.55 | .63 | .59 | .71 | .91 | |
| (11) SBAL – SBAL (R) | 30 | -.003 | .74 | -.75 | .74 | .58 | .65 | .85 | |
| (12) CPH – CPH (L) | 30 | -.05 | .97 | −1.07 | .56 | .84 | .33 | .7 | |
| (13) CPH – CPH (R) | 30 | -.01 | .62 | -.64 | .62 | .65 | .85 | .89 | |
| (14) CH – CH (L) | 30 | .04 | .94 | -.86 | 1.05 | .98 | .80 | .94 | |
| (15) CH – CH (R) | 30 | -.08 | .55 | -.71 | .86 | .90 | .17 | .94 | |
| (16) Overall (1) – (15) | 450 | -.01 | .66 | -.68 | .66 | .68 | .10 | .9 | |
| (17) Upper face | 30 | -.21 | 1.23 | −1.66 | 1.22 | 1.36 | .08 | .96 | |
| (18) Mid-face | 30 | -.33 | 1.4 | −2.05 | 1.54 | 1.63 | .18 | .96 | |
| (19) Lower face | 30 | -.3 | 1.07 | −1.67 | .74 | .95 | .17 | .97 | |
| (20) Overall (Upper face, lower face, Midface) | 90 | -.28 | 1.23 | −1.79 | 1.14 | 1.44 | .007 | .97 | |
Volumetric assessments conducted by the two observers exhibited excellent correlation, with ICC values ranging from 0.96 to 0.97. All photogrammetry-based volumetric measurements, except for measurement (18) (midface), displayed a 95% Bland Altman LoA of ≤ 2 cc. However, the Wilcoxon signed-rank test for paired samples indicated that the deviation across all volumetric distances (20) differed significantly between the two observers (Wilcoxon signed-rank test for paired samples; p = 0.007, n = 90; Table 6).
Figure 9 presents the Bland–Altman plots illustrating the inter-observer reliability of photogrammetry-based measurements.
Fig. 9.
Bland–Altman Plots: Inter-observer reliability of photogrammetry-based measurements. Measurements (1) – (20); Values in millimeters (mm) for measurement (1) – (16) and cubic centimeters (cc) for measurements (17) – (20). MS Excel was used to create the illustration
TrueDepth-based measurements
Table 7 presents the inter-observer reliability of TrueDepth-based measurements. The majority of landmark-to-landmark measurements ((1) – (8) and (10) – (16)) demonstrated good to excellent correlation, with ICC values ranging from 0.64 to 0.97. Measurement (9) showed fair correlation between the two observers. All landmark-to-landmark measurements displayed clinically acceptable 95% Bland–Altman LoA of ≤ 2 mm. The Wilcoxon signed-rank test revealed no statistically significant differences between the two observers for measurements (1) – (15). However, the Wilcoxon signed-rank test indicated a statistically significant difference for the deviation across all landmark-to-landmark distances (16) (Wilcoxon signed-rank test for paired samples; p < 0.001, n = 90; Table 7).
Table 7.
Inter-observer reliability of TrueDepth-based measurements: Bland–Altman analysis, Wilcoxon signed-rank test and Intraclass Correlation Coefficient (ICC). Median values and mean bias in millimeters (mm) for measurements (1) – (16) and in cubic centimeters (cc) for measurements (17) – (20). OP 1 = Observer 1, OP 2 = Observer 2; IBM SPSS 29 was used for data analysis
| Bland–Altman analysis, Wilcoxon signed-rank test for paired samples and ICC | ICC | |||||||
|---|---|---|---|---|---|---|---|---|
| Variables | Bland–Altman | Wilcoxon signed-rank test for paired samples | ||||||
| 95% Confidence Interval | Median | |||||||
| N | Mean bias | Upper Bound | Lower Bound | OP 1 | OP 2 | Asymp. Sig. (2-tailed) | ||
| (1) N – N | 30 | .07 | .99 | -.84 | .64 | .7 | .71 | .88 |
| (2) PRN – PRN | 30 | -.06 | .62 | -.73 | 1.27 | 1.31 | .59 | .93 |
| (3) SN – SN | 30 | -.16 | 1.07 | −1.4 | .6 | .7 | .26 | .64 |
| (4) LS – LS | 30 | -.05 | .81 | -.91 | .79 | .83 | .25 | .88 |
| (5) STO – STO | 30 | -.005 | .59 | -.6 | 1.16 | 1.05 | .59 | .94 |
| (6) LI – LI | 30 | -.03 | .84 | -.91 | .65 | .74 | .14 | .72 |
| (7) GN – GN | 30 | -.001 | .83 | -.83 | 1.02 | .99 | .80 | .92 |
| (8) AL – AL (L) | 30 | -.01 | .83 | -.85 | 1.47 | 1.42 | .58 | .91 |
| (9) AL – AL (R) | 30 | -.1 | 1.31 | −1.5 | 1.47 | 1.68 | .26 | .57 |
| (10) SBAL – SBAL (L) | 30 | -.08 | .77 | -.92 | .82 | .9 | .39 | .85 |
| (11) SBAL – SBAL (R) | 30 | -.06 | .67 | -.80 | 1.14 | 1.05 | .18 | .92 |
| (12) CPH – CPH (L) | 30 | -.11 | .71 | -.94 | .87 | .99 | .08 | .86 |
| (13) CPH – CPH (R) | 30 | .04 | 1.28 | −1.19 | .91 | .87 | .48 | .67 |
| (14) CH – CH (L) | 30 | .01 | .66 | -.64 | 1.15 | 1.12 | .96 | .97 |
| (15) CH – CH (R) | 30 | -.12 | .59 | -.83 | 1.05 | 1.0 | .052 | .95 |
| (16) Overall (1) – (15) | 450 | .04 | .95 | -.86 | .98 | 1.01 | < .001 | .97 |
| (17) Upper face | 30 | -.25 | 3.48 | −3.99 | 2.18 | 2.75 | .32 | .83 |
| (18) Mid-face | 30 | -.01 | 3.05 | −3.06 | 4.42 | 4.2 | .35 | .92 |
| (19) Lower face | 30 | -.12 | 2.94 | −3.17 | 1.23 | 1.3 | .19 | .51 |
| (20) Overall (Upper face, lower face, Midface) | 90 | .13 | 3.39 | −3.14 | 2.12 | 2.5 | .079 | .88 |
For volumetric assessments conducted by the two observers, excellent correlation was observed for measurements (17) (upper face), (18) (midface), and (20) (overall volume). The Wilcoxon signed-rank test revealed no statistically significant differences between the two observers for all volumetric measurements (Table 7). However, all TrueDepth-based volumetric measurements exceeded the clinically acceptable 95% Bland–Altman LoA of ≤ 2 cc between the two observers (Table 7).
Figure 10 displays the Bland–Altman plots for inter-observer reliability of TrueDepth-based measurements.
Fig. 10.
Bland–Altman Plots: Inter-observer reliability of TrueDepth-based measurements. Measurements (1) – (20); Values in millimeters (mm) for measurement (1) – (16) and cubic centimeters (cc) for measurements (17) – (20). MS Excel was used to create the illustration
Discussion
The present study found an overall landmark-to-landmark deviation of M = 0.8 mm (SD = ± 0.58 mm, n = 450) for photogrammetry-based and M = 1.1 mm (SD = ± 0.72 mm, n = 450) for TrueDepth-based SMs (Table 2). Both approaches remained within a clinically acceptable range, exhibiting an overall landmark-to-landmark deviation of ≤ 2 mm. Previous studies align with these findings, reporting a surface-to-surface deviation or landmark-to-landmark deviation of ≤ 2 mm [16, 20, 21]. The mean RMS surface-to-surface deviation for comparable systems (iPhone 11 Pro and 3dMD system) was reported at 0.86 mm ± 0.31 mm by Andrews et al. [16]. Their results indicated that midline points near the mouth and lips demonstrated less accurate results. Nightingale et al. reported an accuracy of 1.3 mm ± 0.3 mm between Artec-based and iPhone 8-based SMs [21]. Seifert et al. observed mean landmark-to-landmark deviations of 1.27 mm for the application Polycam, 1.26 mm for ScandyPro, and 1.45 mm for EM3D when comparing SMs obtained from an iPhone 14 Pro to the 3dMD. They observed that the largest deviations occurred at the stomion for all applications, with values ranging from 1.65 mm for ScandyPro to 2.02 mm for EM3D [20]. They concluded that capturing landmarks in highly flexible or variable facial regions, such as the orolabial region, poses greater challenges for smartphone-based 3D surface imaging. These findings align with the present study’s observations regarding the disparity observed in the left cheilion. Andrews et al. additionally noted that 97% of the distances between landmarks exhibited an average deviation of less than 2 mm. The current trial’s results confirm these findings, when comparing the landmark-to-landmark distances of both smartphone-based approaches to Vectra-based SMs. In contrast, Thurzo et al. observed that certain facial regions exhibited an accuracy of less than 3 mm, when assessing the accuracy of the Bellus3D Dental Pro app, utilizing the TrueDepth camera for facial 3D surface imaging [17]. In particular, the authors identified lower accuracy in deeper structures, specifically in the orbital region, consistent with the observed trend in volumetric differences in the present study. The mid-face, encompassing the orbital region, exhibited the highest volumetric deviation between Vectra M5- and smartphone-based approaches.
In this trial, the overall volumetric accuracy comparing photogrammetry-based SMs to Vectra-based SMs was calculated at M = 1.8 cc (SD = ± 2.12 cc, n = 90; Table 4) and at M = 3.1 cc (SD = ± 2.64 cc, n = 90; Table 4) for TrueDepth-based SMs compared to Vectra-based SMs. The overall accuracy reported in this study aligns with a study conducted by Farook et al., who found a volumetric discrepancy of 4.23 cc ± 2.28 cc comparing SMs of an ear cast obtained by the Oneplus-5t (BBK Electronics, China), the iPhone 6 s (Apple Inc., USA) and the laser scanner 3D Scanner Ultra HD (NextEngine, USA) [31]. However it is known, that the accuracy of anthropometric measurements may vary between smartphone applications, and the precision of SMs is influenced by the scanned object's color and shape [23, 32]. When conducting facial assessments, volumetric results may additionally be influenced by factors such as the inherent difficulty for participants to consistently maintain a neutral facial expression during 3D surface imaging [33, 34]. In addition, volumetric differences in smartphone-based 3D imaging depend on the overall measured volume, with previous investigations indicating an overall measurement error ranging from 0.67% to 3.19% [35]. Further research may contribute to a broader comprehension of smartphones' capability to anticipate volumetric alterations in the facial region.
A constraint of this study pertains to the manual extraction of FAOI, a method that may potentially limit the accuracy of the approach. However, it is essential for aligning the smartphone-based SMs with Vectra M5-based SMs. The procedure was performed consistently with previous investigations [16, 17]. Introducing automation in extracting FAOI could potentially address some of these limitations.
General limitations of 3D surface imaging must also be considered, particularly when incorporating this technology into clinical routines. A critical aspect is the standardization of lighting conditions, as they significantly impact the accuracy of smartphone-based 3D surface imaging. Light reflections can potentially affect landmark detection on the SMs [36]. To address this, the present study was conducted in a room with ambient lighting specifically designed for imaging patients with craniofacial deformities and orthognathic surgery needs. This controlled environment helped mitigate lighting-induced artifacts, a recommendation also supported by previous studies advocating the use of standardized lighting [16, 37, 38].
Another limitation is patients' inherent difficulty in maintaining a neutral facial expression during 3D surface imaging. Previous studies have shown that subtle involuntary contractions in the facial muscles can affect the accuracy of facial landmark detection and volumetric data [34, 37]. Therefore, participants were instructed to maintain a neutral facial expression throughout the imaging process. Future research could investigate stabilization methods or incorporate real-time feedback systems to assist participants in maintaining a consistent facial posture during surface imaging.
Additionally, it is noteworthy to examine the authors’ method of evaluating smartphone-based approaches in relation to their alignment with the gold standard. Aung et al. proposed that deviations exceeding 2 mm from the reference data are clinically unreliable, when comparing anthropometric measurements obtained from SMs generated by an optical surface scanner developed by the Medical Physics Department at the University College Hospital (London, UK) with direct anthropometric measurements [29]. Therefore, this study defined measurements within ≤ 2 units of the reference as clinically acceptable. When applying smartphone-based surface imaging in OMFS, it is important to consider scenarios where deviations above 2 mm or 2 cc are insufficient. These include, among others, intraoperative instrument navigation or precise preoperative planning of orthognathic surgeries, where high accuracy is required for accurate segmentation and repositioning of maxillary or mandibular segments to avoid misalignment [39]. It is important to acknowledge that smartphone-based methods can only be implemented in clinical workflows if certified as medical devices. The software used in this study was utilized in an experimental context and is not yet eligible for routine clinical application.
While smartphone-based 3D surface imaging holds significant potential, addressing variability between smartphone models and software versions is essential to ensure the reliability and generalizability of this study’s results. Several studies have reported varying levels of accuracy across different devices and software [20, 32]. Clinicians are advised to critically evaluate the accuracy of smartphone-based surface imaging software and devices before integrating them into routine clinical workflows. Future studies should focus on standardization and cross-platform validation to enhance clinical applicability.
In addition, the findings of this study regarding inter-observer reliability warrant further discussion. Photogrammetry-based measurements revealed significant differences in volumetric assessments, while TrueDepth-based measurements exceeded the 95% Bland–Altman LoA for inter-observer reliability. These discrepancies may be attributed to the study’s methodology, which required a second observer to manually relocate all landmarks, scale, and align all SMs, making it challenging to reproduce consistent volumetric measurements. This observation underscores the need for further software development to facilitate a fully automated smartphone-based approach, which could enhance reproducibility and ease of use in OMFS.
While smartphone-based 3D surface imaging may not yet fully rival the capabilities of sophisticated 3D surface imaging systems, it can function as a supplementary tool for clinicians, facilitating communication between both patients and fellow healthcare professionals. As technology advances continuously, smartphones can emerge as powerful tools for both patients and surgeons in the future.
Conclusion
This study comprehensively examines two smartphone-based methods for facial 3D surface imaging in alignment with the current gold standard. Smartphone-based approaches using both the TrueDepth camera and photogrammetry exhibited overall landmark-to-landmark distances of ≤ 2 mm that indicated clinically acceptable results in capturing facial features compared to the Vectra M5. Photogrammetry-based SMs generated by smartphones showed higher inter-observer reliability for overall landmark-to-landmark deviation, demonstrated superior alignment, and higher volumetric accuracy with the Vectra-based SMs compared to SMs generated by the TrueDepth camera. Smartphone-based facial 3D surface imaging emerges as a potent tool for clinicians, with oral and maxillofacial surgeons leading its adoption.
Acknowledgements
The authors would like to thank the Regensburg Medical Image Computing lab (ReMIC, OTH Regensburg), the Regensburg Center of Biomedical Engineering Regensburg (OTH Regensburg and Regensburg University), the Chair of Visual Computing (FAU Erlangen-Nürnberg), the Department of Plastic, Aesthetic, Hand and Reconstructive Surgery (Hospital Passau), the Department of Plastic and Reconstructive Surgery Regensburg (University Hospital Regensburg) and the Department of Oral and Maxillofacial Surgery Regensburg (University Hospital Regensburg) for their collaboration and support.
Authors’ contributions
RH: project development, data collection, data analysis and interpretation, manuscript writing; MW: data analysis and interpretation, critical revision of the article; FN: data analysis and interpretation, critical revision of the article; CP: data analysis and interpretation, critical revision of the article; VB: data analysis and interpretation, critical revision of the article; LP: data analysis and interpretation, critical revision of the article; PL: data analysis and interpretation, critical revision of the article; TR: project development, data analysis and interpretation critical revision of the article, final approval; JT: project development, data collection, data analysis and interpretation critical revision of the article, manuscript writing, final approval; TE: project development, data collection, data analysis and interpretation critical revision of the article, manuscript writing, final approval.
Funding
Open Access funding enabled and organized by Projekt DEAL. This study was not funded.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethical approval
The institutional Ethics Committee of the Medical Faculty of the University of Regensburg (DE/EKBY12) (23–3400-101), Germany approved the study (Prof. Edward K. Geissler, PhD, 06/28/2023). All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individuals included in the study.
Consent to Publish declaration
We hereby affirm that informed consent has been obtained from the patient(s) or their legal guardian(s) for the use of their photographs in our manuscript. This consent encompasses all forms of media, including print, digital, and electronic formats, as part of the publication process. The patient(s) or their legal guardian(s) have been thoroughly informed about the nature of the publication, the context in which the photographs will be used, and the potential audience. They have been reassured that their anonymity will be preserved and that no personal or identifiable information will be disclosed. The consent was obtained in a manner that adheres to ethical standards and respects the rights and dignity of the patient(s).
Competing interests
The authors declare no competing interests.
Clinical trial registration number (ZKS Regensburg)
Z-2024–2099-0.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jürgen Taxis and Tobias Ettl contributed equally to the present work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.