A Method for Economical Smartphone‐Based Clinical 3D Facial Scanning

Nightingale, Renee C.; Ross, Maureen T.; Allenby, Mark C.; Woodruff, Maria A.; Powell, Sean K.

doi:10.1111/jopr.13274

Cited by 26 publications

(34 citation statements)

References 25 publications

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“…Table 2 shows the risk of bias and application concerns according to the QUADAS-2 guidelines. Among 23 articles [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ], 14 papers showed an overall low risk of bias [ 40 , 41 , 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 , 56 , 59 , 60 , 61 ], 3 articles had unclear risk of bias [ 39 , 44 , 52 , 57 ] and 4 articles displayed a high risk of bias [ 42 , 54 , 55 , 58 ]. Concerning applicability concerns, all articles showed an overall low level of concern, however, the domain “index test” showed more frequently an unclear concern because some papers did not sufficiently describe the scanning procedures and the used devices [ 40 ,…”

Section: Resultsmentioning

confidence: 99%

“…Concerning applicability concerns, all articles showed an overall low level of concern, however, the domain “index test” showed more frequently an unclear concern because some papers did not sufficiently describe the scanning procedures and the used devices [ 40 , 46 , 55 ]. Regarding the quality assessment, the domain “patient selection” showed the highest risk of bias because of the exclusion of maxillofacial abnormalities [ 42 , 45 , 54 , 55 , 58 ] (this may result in overestimation of diagnostic accuracy [ 38 ]), the small number of participants [ 40 , 41 , 46 , 51 , 52 ] and the unclear method of random sampling [ 40 , 51 , 57 ]. Weighted bar chart for the risk of bias and application concerns of the selected studies is shown in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

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Facial Scanning Accuracy with Stereophotogrammetry and Smartphone Technology in Children: A Systematic Review

et al. 2022

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The aim of the study was to systematically review and compare the accuracy of smartphone scanners versus stereophotogrammetry technology for facial digitization in children. A systematic literature search strategy of articles published from 1 January 2010 to 30 August 2022 was adopted through a combination of Mesh terms and free text words pooled through boolean operators on the following databases: PubMed, Scopus, Web of Science, Cochrane Library, LILACS, and OpenGrey. Twenty-three articles met the inclusion criteria. Stationary stereophotogrammetry devices showed a mean accuracy that ranged from 0.087 to 0.860 mm, portable stereophotogrammetry scanners from 0.150 to 0.849 mm, and smartphones from 0.460 to 1.400 mm. Regarding the risk of bias assessment, fourteen papers showed an overall low risk, three articles had unclear risk and four articles had high risk. Although smartphones showed less performance on deep and irregular surfaces, all the analyzed devices were sufficiently accurate for clinical application. Internal depth-sensing cameras or external infrared structured-light depth-sensing cameras plugged into smartphones/tablets increased the accuracy. These devices are portable and inexpensive but require greater operator experience and patient compliance for the incremented time of acquisition. Stationary stereophotogrammetry is the gold standard for greater accuracy and shorter acquisition time, avoiding motion artifacts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Facial Scanning Accuracy with Stereophotogrammetry and Smartphone Technology in Children: A Systematic Review

et al. 2022

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“…The issue of quantification and the need for reliable methods able to distinguish matches from mismatches remain fundamental in the topic of identification from video surveillance systems. From this point of view, the progressive diffusion of 3D image acquisition systems may provide an innovative contribution, being able to provide a threedimensional facial model, useful for further procedures of comparison [3,[9][10][11][12][13]. Therefore, techniques for 3D-3D superimposition need to be analysed in depth to verify the applicability of this novel technique of analysis to personal identification.…”

Section: Introductionmentioning

confidence: 99%

Improving 3D-3D facial registration methods: potential role of three-dimensional models in personal identification of the living

et al. 2021

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Personal identification of the living from video surveillance systems usually involves 2D images. However, the potentiality of three-dimensional facial models in gaining personal identification through 3D-3D comparison still needs to be verified. This study aims at testing the reliability of a protocol for 3D-3D registration of facial models, potentially useful for personal identification. Fifty male subjects aged between 18 and 45 years were randomly chosen from a database of 3D facial models acquired through stereophotogrammetry. For each subject, two acquisitions were available; the 3D models of faces were then registered onto other models belonging to the same and different individuals according to the least point-to-point distance on the entire facial surface, for a total of 50 matches and 50 mismatches. RMS value (root mean square) of point-to-point distance between the two models was then calculated through the VAM® software. Intra- and inter-observer errors were assessed through calculation of relative technical error of measurement (rTEM). Possible statistically significant differences between matches and mismatches were assessed through Mann–Whitney test (p < 0.05). Both for intra- and inter-observer repeatability rTEM was between 2.2 and 5.2%. Average RMS point-to-point distance was 0.50 ± 0.28 mm in matches, 2.62 ± 0.56 mm in mismatches (p < 0.01). An RMS threshold of 1.50 mm could distinguish matches and mismatches in 100% of cases. This study provides an improvement to existing 3D-3D superimposition methods and confirms the great advantages which may derive to personal identification of the living from 3D facial analysis.

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“…Nightingale et al investigated the reliability of using smartphone cameras and photogrammetry software to obtain face scans in 2020. They reported strong reliability even for inexperienced operators 18 . Thus, the use of smartphone scanners for face scans seems promising.…”

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confidence: 99%

“…They reported strong reliability even for inexperienced operators. 18 Thus, the use of smartphone scanners for face scans seems promising. However, there is no available study investigating if smartphone face scans are reliable for virtual facebow records.…”

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confidence: 99%

Trueness and Precision of Economical Smartphone‐Based Virtual Facebow Records

Chen

Decker

et al. 2021

Journal of Prosthodontics

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Purpose To investigate the trueness and precision of virtual facebow records using a smartphone as a three‐dimensional (3D) face scanner. Material and Methods Twenty repeated virtual facebow records were performed on two subjects using a smartphone as a 3D face scanner. For each subject, a virtual facebow was attached to his/her maxillary arch, and face scans were performed using a smartphone with a 3D scan application. The subject's maxillary arch intraoral scan was aligned to the face scan by the virtual facebow fork. This procedure was repeated 10 times for each subject. To investigate if the maxillary scan is located at the right position to the face, these virtual facebow records were superimposed to a cone‐beam computed tomography (CBCT) head scan from the same subject by matching the face scan to the 3D face reconstruction from CBCT images. The location of maxillary arch in virtual facebow records was compared with its position in CBCT. The “trueness” of the proposed procedure is defined as the deviation between maxilla arch position in virtual facebow records and the CBCT images. The “precision” is defined as the deviation between each virtual facebow record. The linear deviation at left central incisor (#9), left first molar (#14), and right first molar (#3), as well as angular deviation of occlusal plane were analyzed with descriptive statistics. Differences between two objects were also explored with Mann Whitney U test. Results The 20 virtual facebow records using the smartphone 3D scanner deviated from the CBCT measurements (trueness) by 1.14 ± 0.40 mm at #9, 1.20 ± 0.50 mm at #14, 1.12 ± 0.51 mm at the #3, and 1.48 ± 0.56° in the occlusal plane. The VFTs deviated from each other by 1.06 ± 0.50 mm at #9, 1.09 ± 0.49 mm at #14, 1.11 ± 0.58 mm at #3, and 0.81 ± 0.58° in the occlusal plane. When all sites combined, the trueness was 1.14 ± 0.40 mm, and the precision was 1.08 ± 0.52 mm. Out of eight measurements, three measurements were significantly different between subjects. Nevertheless, the mean difference was small. Conclusions Virtual facebow records made using smartphone‐based face scan can capture the maxilla position with high trueness and precision. The deviation can be anticipated as around 1 mm in linear distance and 1° in angulation.

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A Method for Economical Smartphone‐Based Clinical 3D Facial Scanning

Cited by 26 publications

References 25 publications

Facial Scanning Accuracy with Stereophotogrammetry and Smartphone Technology in Children: A Systematic Review

Facial Scanning Accuracy with Stereophotogrammetry and Smartphone Technology in Children: A Systematic Review

Improving 3D-3D facial registration methods: potential role of three-dimensional models in personal identification of the living

Trueness and Precision of Economical Smartphone‐Based Virtual Facebow Records

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