Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

Comparative evaluation of novel framework-supported 3-dimensional facial scanning using smartphone device with freehand facial scanning in patients seeking orthodontic treatment: A cross-sectional study

INTRODUCTION

Surface anthropometric assessment of soft tissues is an ideal approach for measuring 3D facial changes with smartphone/tablet-based applications revolutionizing 3D facial acquisition. However, the scans obtained are prone to distortion and have limited repeatability due to the freehand recording of the scans in continuous image capture mode, thus also reducing their reliability. The aim was to introduce the design and operation of an innovative apparatus for acquiring 3D facial scans in a standardised, repeatable, and convenient way for young children and adults.

MATERIAL AND METHODS

The apparatus presents a framework with a straight and scissor arm with the recommended dimension of 68×60×34cm with a 360-degree rotatory joint similar to wall-mounted X-ray systems used in dental offices. Facial scans of 15 patients aged between 19-25 years (mean age=23.13 years) were recorded using the two techniques (framework-supported [SF] and freehand [SWF]) Scandy Pro app in Apple iPad Pro. The scans were exported in .stl format and analysed using Meshlab and Viewbox 4 software for surface comparison, scan time, and mean absolute distance (MAD) between facial soft tissue landmarks.

RESULTS

Scans using the framework (SF) showed fewer aberrations, especially in the nasolabial and periorbital areas. Zygoma R and L (0.608±1.605 and 0.503±1.191 respectively) displayed the most difference, while Point A (0.323±1.381), Pogonion (0.364±1.344), and infraorbital region R and L (0.307±0.785 and 0.362±1.089 respectively) displayed the least. With no scan interruptions, the average scan time decreased threefold to 10.14seconds for SF compared to 27.81seconds for SWF, with 12 instances of tracking loss. Superimposition analysis of SF scans shows ICC values from 0.574 to 0.882, indicating good agreement.

CONCLUSION

The proposed framework provides a reliable, accurate, and cost-effective alternative for 3D facial imaging using smartphone devices. It demonstrates high reproducibility and significant reductions in scan time and tracking loss. This apparatus could facilitate the routine clinical use of 3D facial scanning in orthodontics, offering portable and non-invasive solutions.

Evaluation of the accuracy and usability of facial scans obtained with smartphones by different users

INTRODUCTION

This study aims to compare 3-dimensional facial scan images obtained by patients and clinicians using a smartphone with the patients' actual facial dimensions.

METHODS

With the patient's head in a stabilized position, patients were informed about the facial scanning process and instructed to perform a self-scan (group 1). The second scan was conducted by a clinician (group 2). Scanning times were recorded in groups 1 and 2. Finally, 17 direct anatomic measurements were taken from the patients (group 3). The acquired scan images were transferred to computer software, in which the anthropometric measurements were recalibrated and repeated virtually. The data obtained were statistically evaluated using the Shapiro-Wilk, t, Kruskal-Wallis, and Bonferroni tests.

RESULTS

According to Bonferroni tests, statistically significant differences were observed in groups 1 and 3 between Sto-Ls and Sn-Me', Al(R)-Al(L), T(L)-Prn, Go(R)-Go(L), Sto-Me'; between Go(R)-Go(L) and Sto-Me', Ch(R)-Ch(L), Sto-Li; and between Sto-Me' and Sn-Sto, N-Sn groups. In groups 2 and 3, significant differences were noted between Sto-Ls and En(R)-En(L), Ex(R)-Ex(L), Sn-Me', Zyg(R)-Zyg(L), Sto-Me', Go(R)-Go(L); and between Sto-Li and Ex(R)-Ex(L), Sub-Me', Zyg(R)-Zyg(L), Sto-Me', Go(R)-Go(L) (P <0.05). In addition, in groups 1 and 3, measurements such as Sto-Me', Go(R)-Go(L), Me'-C, and T(L)-Prn showed an average difference of ≥1.00 mm between groups. In groups 2 and 3, Go(R)-Go(L), Sto-Me', Zyg(R)-Zyg(L), and Me'-C measurements showed an average difference of >1.00 mm between groups.

CONCLUSIONS

Facial scan images obtained with smartphones show an acceptable level of similarity to real measurements. Subjects without experience in facial scanning were able to perform facial scans at a high level of accuracy with the given instructions.

Trueness and precision of a handheld, a desktop and a mobile 3D face scanning system: An in vitro study

OBJECTIVE

This in vitro study investigated the trueness and precision of three different face scanning systems: a handheld, a desktop and a mobile 3D face scanning system.

MATERIAL AND METHODS

Fourteen landmarks were placed on a mannequin head, and sixteen inter-landmark distances were measured using a digital vernier caliper, repeated 20 times over 80 days. Three 3D face scanning systems were evaluated: a handheld (Metismile; Shining 3D Tech Co., Hangzhou, China), a desktop (RAYFace v2.0; Ray Co., Ltd., Gyeonggi-do, Korea), and a mobile application (Heges, Simon Marinek) on a smart device (iPad Pro X, Apple Inc., Cupertino, CA). Sixty facial scans were analyzed using metrology software (Geomagic Control X), and inter-landmark distances were compared to anthropometric measurements. Trueness was determined by absolute linear deviation and analyzed using one-way ANOVA, with Bonferroni and Tamhane tests for significant variance. Precision was compared to anthropometric measurements and analyzed using Kruskall-Wallis test.

RESULTS

3D analysis showed that the handheld scanner had the highest trueness (0.18 ± 0.15 mm) and precision (0.22 ± 0.04 mm). The desktop scanner had a trueness of 0.35 ± 0.26 mm and precision of 0.61 ± 0.18 mm, while the mobile scan application had a trueness of 0.54 ± 0.34 mm and precision of 0.47 ± 0.12 mm. All systems showed the highest trueness for vertical measurements compared to horizontal measurements. In the lower face, the precision was higher than anthropometric measurements for all 3D face scanning systems.

CONCLUSIONS

The handheld scanner demonstrated the highest trueness and its precision surpassed anthropometric measurements. The desktop scanner outperformed the mobile scan application in trueness but not in precision.

CLINICAL SIGNIFICANCE

The handheld, the desktop and the mobile face scanning system showed clinically acceptable trueness (< 0.6 mm) and could be used for virtual facebow transfer. All 3D face scanning systems in the present study demonstrated superior precision in the lower face compared to anthropometric measurements.

Influence of ambient light on the accuracy of different face scanning methods: an in-vitro study

BACKGROUND

Face scanners provide a viable method for capturing a patient's face geometry. To optimize their accuracy, influencing factors, like the ambient light, need to be examined.

METHODS

A human head model with eight pins attached to its surface was used to investigate the accuracy of four face scanning methods (Face Hunter, iPad, Medit i700, single camera photogrammetry) under three illumination levels (500 lx, 5000 lx, 20 000 lx). An industrial CT scan was used as reference. Two alignment-areas - full face (AL-FF) and spheres (AL-KG) and two investigation areas - center face (UB-CF) and full face (UB-FF), were used during the examination. The root-mean-square-error (RMSE) was employed as a measure. Separated by trueness and precision, a one-way ANOVA was performed with post hoc Games-Howell tests for each scanning method.

RESULTS

All scanners showed significant differences between the illumination levels. For most test groups, the Face Hunter acquired its lowest RMSE values under 500 lx. The same can be said for the Medit i700, even though for trueness, differences to 5000 lx were random. Single camera photogrammetry performed better at higher illumination levels, but only random differences between 5000 lx and 20 000 lx were seen. For the iPad, different results for optimal illumination were found regarding trueness and precision, as well as the investigation areas. All accuracy results were labelled as highly reliable, except for the iPad´s trueness results.

CONCLUSION

Scanner-dependent influence of ambient light was shown in this in-vitro study. Face Hunter and Medit i700 performed better under a darker illumination of 500 lx, whereas single camera photogrammetry needed brighter lighting. For the iPad no tested lighting situation showed clear advantages.

Evaluating smartphone-based 3D imaging techniques for clinical application in oral and maxillofacial surgery: A comparative study with the vectra M5

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.

The Orientation in Space of the Maxillary Arch: New and Old Devices in the Prosthetic Digital Workflow

OBJECTIVE

In extensive prosthetic rehabilitations and in those involving the anterior area, a correct 3D spatial position is of fundamental importance for effective communication between the clinician and the dental technician. The aim of this article is to analyze the different methods used to position in space and/or in an articulator the maxillary arch in analog workflows highlighting shortcomings and difficulties in order to understand how to overcome them when employing digital workflows.

OVERVIEW

Traditional mechanical devices, such as anatomical, kinematic, esthetic and postural facebows, have clear indications, but also limitations, especially in cases of skeletal asymmetries. Modern digital tools, including photography, CBCT, facial scanners and jaw recording devices, are here critically analyzed to illustrate the advantages of working in a virtual space.

CONCLUSIONS

The adoption of digital tools in the prosthetic workflow represents a significant improvement compared to traditional techniques, as it reduces errors and artifacts of registration and transfer of the position of the maxillary arch in the articulator space. This contributes to more predictable esthetic and functional results, with a positive impact for clinicians and technicians, improving clinical-laboratory communication, operational efficiency and overall quality of work.

CLINICAL SIGNIFICANCE

The integration of digital tools into prosthetic workflows represents an important advancement in clinical practice since they reduce human error and facilitate communication between the clinician and the laboratory. When carrying out rehabilitations involving changes in esthetics or occlusal plane orientation, the proper and accurate positioning of the upper arch in space is particularly relevant.

Smartphone-generated 3D facial images: reliable for routine assessment of the oronasal region of patients with cleft or mere convenience? A validation study

OBJECTIVES

To evaluate the validity and reliability of smartphone-generated three-dimensional (3D) facial images for routine evaluation of the oronasal region of patients with cleft by comparing their accuracy to that of direct anthropometry (DA) and 3dMD.

MATERIALS AND METHODS

Eighteen soft-tissue facial landmarks were manually labelled on each of the 17 (9 males and 8 females; mean age 23.3 ± 5.4 years) cleft lip and palate (CLP) patients' faces. Two surface imaging systems, 3dMDface and Bellus3D FaceApp, were used to perform two imaging operations on each labelled face. Subsequently, 32 inter-landmark facial measurements were directly measured on the labelled faces and digitally measured on the 3D facial images. Statistical comparisons were made between smartphone-generated 3D facial images (SGI), DA, and 3dMD measurements.

RESULTS

The SGI measurements were slightly higher than those from DA and 3dMD, but the mean differences between inter-landmark measurements were not statistically significant across all three methods. In terms of clinical acceptability, 16% and 59% of measures showed differences of ≤ 3 mm or ≤ 5º, with good agreement between DA and SGI and 3dMD and SGI, respectively. A small systematic bias of ± 0.2 mm was observed generally among the three methods. Additionally, the mean absolute difference between the DA and SGI methods was the highest for linear measurements (1.31 ± 0.34 mm) and angular measurements (4.11 ± 0.76º).

CONCLUSIONS

SGI displayed fair trueness compared to DA and 3dMD. It exhibited high accuracy in the orolabial area and specific central and flat areas within the oronasal region. Notwithstanding this, it has limited clinical applicability for assessing the entire oronasal region of patients with CLP. From a clinical application perspective, SGI should accurately encompass the entire oronasal region for optimal clinical use.

CLINICAL RELEVANCE

SGI can be considered for macroscopic oronasal analysis or for patient education where accuracy within 3 mm and 5º may not be critical.

Smartphone applications for facial scanning: A technical and scoping review

INTRODUCTION

Facial scanning through smartphone scanning applications (SSA) is increasingly being used for medical applications as cost-effective, chairside method. However, clinical validation is lacking. This review aims to address: (1) Which SSA could perform facial scanning? (2) Which SSA can be clinically used? (3) Which SSA have been reported and scientifically validated for medical applications?

METHODS

Technical search for SSA designed for face or object scanning was conducted on Google, Apple App Store, and Google Play Store from August 2022 to December 2023. Literature search was performed on PubMed, Cochrane, EMBASE, MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, Clinicaltrials.gov, ICTRP (WHO) and preprints up to 2023. Eligibility criteria included English-written scientific articles incorporating at least one SSA for clinical purposes. SSA selection and data extraction were executed by one reviewer, validated by second, with third reviewer being consulted for discordances.

RESULTS

Sixty-three applications designed for three-dimensional object scanning were retrieved, with 52 currently offering facial scanning capabilities. Fifty-six scientific articles, comprising two case reports, 16 proof-of-concepts and 38 experimental studies were analysed. Thirteen applications (123D Catch, 3D Creator, Bellus 3D Dental Pro, Bellus 3D Face app, Bellus 3D Face Maker, Capture, Heges, Metascan, Polycam, Scandy Pro, Scaniverse, Tap tap tap and Trnio) were reported in literature for digital workflow integration, comparison or proof-of-concept studies.

CONCLUSION

Fifty-two SSA can perform facial scanning currently and can be used clinically, offering cost-effectiveness, portability and user-friendliness. Although clinical validation is crucial, only 13 SSA were scientifically validated, underlying awareness of potential pitfalls and limitations.

Accuracy Evaluation of a Three-Dimensional Face Reconstruction Model Based on the Hifi3D Face Model and Clinical Two-Dimensional Images

Three-dimensional (3D) facial models have been increasingly applied in orthodontics, orthognathic surgery, and various medical fields. This study proposed an approach to reconstructing 3D facial models from standard orthodontic frontal and lateral images, providing an efficient way to expand 3D databases. A total of 23 participants (average age 20.70 ± 5.36 years) were enrolled. Based on the Hifi3D face model, 3D reconstructions were generated and compared with corresponding face scans to evaluate their accuracy. Root mean square error (RMSE) values were calculated for the entire face, nine specific facial regions, and eight anatomical landmarks. Clinical feasibility was further assessed by comparing six angular and thirteen linear measurements between the reconstructed and scanned models. The RMSE of the reconstruction model was 2.00 ± 0.38 mm (95% CI: 1.84-2.17 mm). High accuracy was achieved for the forehead, nose, upper lip, paranasal region, and right cheek (mean RMSE < 2 mm). The forehead area showed the smallest deviation, at 1.52 ± 0.88 mm (95% CI: 1.14-1.90 mm). In contrast, the lower lip, chin, and left cheek exhibited average RMSEs exceeding 2 mm. The mean deviation across landmarks was below 2 mm, with the Prn displaying the smallest error at 1.18 ± 1.10 mm (95% CI: 0.71-1.65 mm). The largest discrepancies were observed along the Z-axis (Z > Y > X). Significant differences (p < 0.05) emerged between groups in the nasolabial, nasal, and nasofrontal angles, while the other 13 linear and 3 angular measurements showed no statistical differences (p > 0.05). This study explored the feasibility of reconstructing accurate 3D models from 2D photos. Compared to facial scan models, the Hifi3D face model demonstrated a 2 mm deviation, with potential for enriching 3D databases for subjective evaluations, patient education, and communication. However, caution is advised when applying this model to clinical measurements, especially angle assessments.

Accuracy of Smartphone-Based Three-Dimensional Facial Scanning System: A Systematic Review

BACKGROUND

Recently, the integration of 3D face scanning into smartphones has raised vast interest in plastic surgery. With the release of smartphones featuring 3D face scanning technology, users now can capture detailed 3D models of their faces using their smartphones. However, trueness and precision of this system is less well established.

METHODS

PubMed, Cochrane Library, Embase, ScienceDirect, Scopus, and Web of Science databases were searched for studies evaluating 3D scanning of smartphone devices and conventional 3D imaging systems from January 1, 2017, to June 1, 2023. A qualitative systematic review was conducted by two review authors after independently selecting studies, extracting data, and assessing the risk of bias of included studies.

RESULTS

A total of 11 studies were included, all focusing on the accuracy of smartphone 3D facial scanning. The results show that although smartphones perform poorly on deep and irregular surfaces, they are accurate enough for clinical applications and have the advantage of being economical and portable.

CONCLUSIONS

Smartphone-based 3D facial scanning has been basically validated for clinical application, showing broad clinical application prospects in plastic surgery.

LEVEL OF EVIDENCE II

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors   www.springer.com/00266 .

The Validity of a Smartphone-Based Method for Acquiring 3D Images of the Face

Objectives. To evaluate the accuracy and reproducibility of measurements obtained using the Bellus3D Face Application on a mobile smartphone by comparing them to direct measurements on pre-marked and blank face scans. Materials and Methods. Twenty-five healthy young adults (six males and nineteen females; age range 20-30 years) were included in this prospective cross-sectional study, with the only exclusion criterion being the presence of significant facial hair interfering with the placement and visualization of landmarks. Image acquisitions were performed using an iPhone XR with the Bellus3D FaceApp face scanning application, an iOS application for smartphones. Ten single midfacial and five paired bilateral landmarks were defined and marked. Two face scans were performed on each patient, both on blank and marked faces, and distances were measured directly with calipers and digitally. Results. The random error values were 1.0 mm and 0.4 mm for the manual point placement and measurements and virtual point placement on blank faces, respectively. The two methods used (the direct method and acquisition on faces with landmarks) demonstrate relatively similar reliability (ICC > 0.8); however, a paired t-test showed that the differences between several measurements were statistically significant (p < 0.05). Regardless of the method used, there was a systematic error for various values that included the nose and mouth (p < 0.05). The measurements demonstrating the most significant differences between the methods were those that included the tip of the nose, with the mean differences being -4.4-3.3 mm. The measurements of the distances that estimate face "depth" showed the greatest consistency irrespective of the tested method (p > 0.05 and ICC > 0.8). Conclusions. The use of the Bellus3D FaceApp is precise and reproducible for certain areas of the face, but digital reconstruction errors prohibit, for the time being, the use of this technology in everyday clinical practice. The noted discrepancies were consistent and more prevalent for specific areas such as the tip of the nose. Further investigations are required to determine other sources of error and for other smartphone-based applications released for 3D face image acquisitions.

Accuracy of Complete-Arch Scans Obtained by Intraoral Scanner and Smartphone Three-Dimensional Scanning Applications With Different Smartphone Position Setups: An In Vitro Study

INTRODUCTION

The high cost of intraoral scanners (IOS) for complete-arch scans makes them less accessible for many dental practitioners. As a viable alternative, smartphone scanner applications (SMP) provide comparable scanning capabilities at a significantly low cost. However, there is limited data on the accuracy of SMP, especially when used in various smartphone positions. This study aimed to compare the three-dimensional (3D) and linear accuracy of complete-arch scans acquired by an IOS and SMP (KIRI Engine, KIRI Innovations, Guangdong, China) at three shooting angles (0°, 45°, and 90° for SMP_3A) and two shooting angles (30° and 60° for SMP_2A).

METHODS

A stone dental cast was scanned with a laboratory scanner as a reference, with 11 scans performed by an IOS, SMP_2A, and SMP_3A. In 3D analysis, trueness and precision were evaluated through superimposition with the reference scan and within each group, respectively, using the best-fit algorithm of Geomagic Wrap software (3D Systems, Inc., Rock Hill, SC). Trueness in linear discrepancy was assessed by comparing the occlusal-cervical and mesiodistal dimensions of reference teeth (canine, premolar, and molar), intercanine width, and intermolar width on the digital casts to measurements of the stone cast, while precision was measured using the coefficient of variance. Differences between groups were analyzed using the Friedman test, followed by the Dunn-Bonferroni post hoc test with a significance level set at 0.05.

RESULTS

IOS exhibited significantly lower trueness than SMP_2A (p = 0.003) with significantly greater width discrepancies on canines (p = 0.001) and molars (p < 0.001). Discrepancy patterns differed among the three scanning methods. The IOS showed greater discrepancies on the occlusal surfaces of posterior teeth. While SMP_3A demonstrated higher variation on the palatal surfaces and interproximal areas of posterior teeth. For precision, SMP_3A (p = 0.028) and SMP_2A (p = 0.003) showed a significantly lower precision in 3D analysis, but a comparable reproducibility in linear measurement to IOS.

CONCLUSION

TRIOS IOS (3Shape, Copenhagen, Denmark) exhibited lower trueness in 3D and linear accuracy analyses for complete-arch scans. The positions of the smartphone significantly enhanced trueness at the undercut region. SMP_2A and SMP_3A can be a potential alternative for precise linear measurement in complete-arch scans with selective use.

"Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry"

Background

The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging.In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system.

Methods

30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland-Altman analysis, and calculation of the intraclass correlation coefficient (ICC).

Results

All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland-Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements.

Conclusion

Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy.

Machine learning-based decision support system for orthognathic diagnosis and treatment planning

BACKGROUND

Dento-maxillofacial deformities are common problems. Orthodontic-orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning-based decision support system for treatment of dento-maxillofacial malformations.

METHODS

Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes.

RESULTS

The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human-computer interaction. There was no statistically significant difference between the actual and AI- groups.

CONCLUSIONS

Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.

Comparison of three-dimensional imaging of the nose using three different 3D-photography systems: an observational study

BACKGROUND

New 3D technologies for superficial soft tissue changes, especially in plastic and reconstructive surgical procedures, can improve the planning and documentation of facial surgeries. The purpose of this study was to compare and determine the applicability and feasibility of three different 3D-photography systems in clinical practice imaging the nose.

METHODS

A total of 16 healthy non-operated noses were included in this prospective study. A plaster model of each nose was produced, digitized, and converted to a .stl mesh (= ground truth model). Three-dimensional images of each nose were then taken using Artec Space Spider (gold standard), Planmeca ProFace®, and the Bellus3D Dental Pro application. All resulting .stl files were aligned to the ground truth model using MeshLab software, and the root mean square error (RMSE), mean surface distance (MSD), and Hausdorff distance (HD) were calculated.

RESULTS

The Artec Space Spider 3D-photography system showed significantly better results compared to the two other systems in regard to RMSE, MSD, and HD (each p < 0.001). There was no significant difference between Planmeca ProFace® and Bellus3D Dental Pro in terms of RMSE, MSD, and HD. Overall, all three camera systems showed a clinically acceptable deviation to the reference model (range: -1.23-1.57 mm).

CONCLUSIONS

The three evaluated 3D-photography systems were suitable for nose imaging in the clinical routine. While Artec Space Spider showed the highest accuracy, the Bellus3D Dental Pro app may be the most feasible option for everyday clinical use due to its portability, ease of use, and low cost. This study presents three different systems, allowing readers to extrapolate to other systems when planning to introduce 3D photography in the clinical routine.

Impact of orthodontic-induced facial morphology changes on aesthetic evaluation: a retrospective study

BACKGROUND

The profound influence of orthodontic treatments on facial aesthetics has been a topic of increasing interest. This study delves into the intricate interplay between orthodontic treatments, facial feature alterations, and aesthetic perceptions.

METHODS

A total of 73 patients who had undergone orthodontic treatment were included in this study. Facial photographs were taken before and after treatment. Ten orthodontists provided facial aesthetic ratings (FAR) for each patient's frontal, profile, and overall views. 48 facial landmarks were manually placed by the orthodontists and normalized using Generalized Procrustes analysis (GPA). Two types of phenotypes were derived from facial landmarks. Global facial phenotypes were then extracted using principal component analysis (PCA). Additionally, 37 clinical features related to aesthetics and orthodontics were extracted. The association between facial features and changes in FAR after orthodontic treatment was determined using these two types of phenotypes.

RESULTS

The FAR exhibited a high correlation among orthodontic experts, particularly in the profile view. The FAR increased after orthodontic treatment, especially in profile views. Extraction of premolars and orthognathic surgery were found to result in higher FAR change. For global facial phenotypes, the most noticeable changes in the frontal and profile views associated with FAR occurred in the lip area, characterized by inward retraction of the lips and slight chin protrusion in the profile view, as well as a decrease in lip height in the frontal view. The changes observed in the profile view were statistically more significant than those in the frontal view. These facial changes were consistent with the changes from orthodontic treatment. For clinical features, two profile features, namely pg.sm.hori and pg.n.ls, were found to be associated with FAR following orthodontic treatment. The highest FAR scores were achieved when pg.sm.hori was at 80° and pg.n.ls was at 8°. On the other hand, frontal clinical features had a subtle effect on FAR during orthodontic treatment.

CONCLUSIONS

This study demonstrated that orthodontic treatment improves facial aesthetics, particularly at lip aera in the profile view. Profile clinical features, such as pg.sm.hori and pg.n.ls, are essential in orthodontic treatment which could increase facial aesthetics.

Can smartphones be used for routine dental clinical application? A validation study for using smartphone-generated 3D facial images

OBJECTIVES

To compare the accuracy of smartphone-generated three-dimensional (3D) facial images to that of direct anthropometry (DA) and 3dMD with the aim of assessing the validity and reliability of smartphone-generated 3D facial images for routine clinical applications.

MATERIALS AND METHODS

Twenty-five anthropometric soft-tissue facial landmarks were labelled manually on 22 orthognathic surgery patients (11 males and 11 females; mean age 26.2 ± 5.3 years). For each labelled face, two imaging operations were performed using two different surface imaging systems: 3dMDface and Bellus3D FaceApp. Next, 42 inter-landmark facial measurements amongst the identified facial landmarks were measured directly on each labelled face and also digitally on 3D facial images. The measurements obtained from smartphone-generated 3D facial images (SGI) were statistically compared with those from DA and 3dMD.

RESULTS

SGI had slightly higher measurement values than DA and 3dMD, but there was no statistically significant difference between the mean values of inter-landmark measures across the three methods. Clinically acceptable differences (≤3 mm or ≤5°) were observed for 67 % and 74 % of measurements with good agreement between DA and SGI, and 3dMD and SGI, respectively. An overall small systematic bias of ± 0.2 mm was observed between the three methods. Furthermore, the mean absolute difference between DA and SGI methods was highest for linear (1.41 ± 0.33 mm) as well as angular measurements (3.07 ± 0.73°).

CONCLUSIONS

SGI demonstrated fair trueness compared to DA and 3dMD. The central region and flat areas of the face in SGI are more accurate. Despite this, SGI have limited clinical application, and the panfacial accuracy of the SGI would be more desirable from a clinical application standpoint.

CLINICAL SIGNIFICANCE

The usage of SGI in clinical practice for region-specific macro-proportional facial assessment involving central and flat regions of the face or for patient education purposes, which does not require accuracy within 3 mm and 5° can be considered.

Validation of three-dimensional facial imaging captured with smartphone-based photogrammetry application in comparison to stereophotogrammetry system

Statement of problem

The development of facial scanners has improved capabilities to create three-dimensional (3D) virtual patients for accurate facial and smile analysis. However, most of these scanners are expensive, stationary and involve a significant clinical footprint. The use of the Apple iPhone and its integrated "TrueDepth" near-infrared (NIR) scanner combined with an image processing application (app) offers the potential to capture and analyze the unique 3D nature of the face; the accuracy and reliability of which are yet to be established for use in clinical dentistry.

Purpose

This study was designed to validate both the trueness and precision of the iPhone 11 Pro smartphone TrueDepth NIR scanner in conjunction with the Bellus3D Face app in capturing 3D facial images in a sample of adult participants in comparison to the conventional 3dMDface stereophotogrammetry system.

Material and methods

Twenty-nine adult participants were prospectively recruited. Eighteen soft tissue landmarks were marked on each participant's face before imaging. 3D facial images were captured using a 3dMDface system and the Apple iPhone TrueDepth NIR scanner combined with the Bellus3D Face app respectively. The best fit of each experimental model to the 3dMD scan was analyzed using Geomagic Control X software. The root mean square (RMS) was used to measure the "trueness" as the absolute deviation of each TrueDepth scan from the reference 3dMD image. Individual facial landmark deviations were also assessed to evaluate the reliability in different craniofacial regions. The "precision" of the smartphone was tested by taking 10 consecutive scans of the same subject and comparing those to the reference scan. Intra-observer and inter-observer reliabilities were assessed using the intra-class correlation coefficient (ICC).

Results

Relative to the 3dMDface system, the mean RMS difference of the iPhone/Bellus3D app was 0.86 ± 0.31 mm. 97% of all the landmarks were within 2 mm of error compared with the reference data. The ICC for intra-observer reproducibility or precision of the iPhone/Bellus3D app was 0.96, which was classified as excellent. The ICC for inter-observer reliability was 0.84, which was classified as good.

Conclusions

These results suggest that 3D facial images acquired with this system, the iPhone TrueDepth NIR camera in conjunction with the Bellus3D Face app, are clinically accurate and reliable. Judicious use is advised in clinical situations that require high degrees of detail due to a lack of image resolution and a longer acquisition time. Generally, this system possesses the potential to serve as a practical alternative to conventional stereophotogrammetry systems for use in a clinical setting due to its accessibility and relative ease of use and further research is planned to appraise its updated clinical use.