Test-retest reliability of smile tasks using three-dimensional facial topography

Tanikawa, Chihiro; Takada, Kenji

doi:10.2319/062617-425.1

Cited by 16 publications

(16 citation statements)

References 21 publications

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“…Studies on related topics in various fields have also been gaining more popularity. 3,[11][12][13][14] Although three-dimensional images have gained popularity these days, [15][16][17][18][19] two-dimensional cephalometric analysis is still a vital tool in orthodontic diagnosis and treatment planning since it provides information regarding a patient's skeletal and soft tissue. Currently, computer-assisted cephalometric analysis eliminates human-induced mechanical errors.…”

Section: Discussionmentioning

confidence: 99%

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Hwang¹,

Park²,

Moon³

et al. 2019

The Angle Orthodontist

155

112

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Objectives: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. Materials and Methods: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. Results: Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. Conclusions: AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.

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Section: Discussionmentioning

confidence: 99%

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Hwang¹,

Park²,

Moon³

et al. 2019

The Angle Orthodontist

155

112

View full text Add to dashboard Cite

show abstract

“…They were then asked to perform two tasks: (1) to form a resting facial expression, and (2) to form the peak of a posed smile. The accuracy of the facial expressions was described in previous studies [12, 13]. The subjects were instructed vocally for each task and asked to maintain the expressions for about 2 seconds.…”

Section: Methodsmentioning

confidence: 99%

“…As described in detail previously [12, 13], each 3D facial image was displayed on a 17-in LCD monitor (1701FP, Dell, Inc., Round Rock, TX, USA), scaled down to 75% of its actual size. The positions of 10 single and 8 paired landmarks (glabella [Gla], nasion [N], exocanthion [Ex], endocanthion [En], palpebrale superius [Ps], palpebrale inferius [Pi], porion [Po], orbitale [Or], pronasale [Prn], alar curvature point [Ac], subnasale [Sn], labiale superius [Ls], stomion [Sto], cheilion [Ch], labiale inferius [Li], submentale [Sm], pogonion [Pog], gnathion [Gn]) [14] (S1 Fig and S1 Table) were identified by visual inspection of the image using a computer mouse cursor, and digitized using commercial software (Face Rugle, Medic Engineering Co., Kyoto, Japan).…”

Section: Methodsmentioning

confidence: 99%

Functional decline in facial expression generation in older women: A cross-sectional study using three-dimensional morphometry

et al. 2019

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Elderly people show a decline in the ability to decode facial expressions, but also experience age-related facial structure changes that may render their facial expressions harder to decode. However, to date there is no empirical evidence to support the latter mechanism. The objective of this study was to assess the effects of age on facial morphology at rest and during smiling, in younger (n = 100; age range, 18–32 years) and older (n = 30; age range, 55–65 years) Japanese women. Three-dimensional images of each subject’s face at rest and during smiling were obtained and wire mesh fitting was performed on each image to quantify the facial surface morphology. The mean node coordinates in each facial posture were compared between the groups using t-tests. Further, the node coordinates of the fitted mesh were entered into a principal component analysis (PCA) and a multifactor analysis of variance (MANOVA) to examine the direct interactions of aging and facial postures on the 3D facial morphology. The results indicated that there were significant age-related 3D facial changes in facial expression generation and the transition from resting to smiling produced a smaller amount of soft tissue movement in the older group than in the younger group. Further, 185 surface configuration variables were extracted and the variables were used to create four discriminant functions: the age-group discrimination for each facial expression, and the facial expression discrimination for each age group. For facial expression discrimination, the older group showed 80% accuracy with 2 of 66 significant variables, whereas the younger group showed 99% accuracy with 15 of 144 significant variables. These results indicate that in both facial expressions, the facial morphology was distinctly different in the younger and older subjects, and that in the older group, the facial morphology during smiling could not be as easily discriminated from the morphology at rest as in the younger group. These results may help to explain one aspect of the communication dysfunction observed in older people.

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“…Homogeneous modeling. For each facial surface, tting of high-resolution template meshes or a generic model 57,58 was performed using commercial software (HBM-Rugle, Medic Engineering Co., Kyoto) based on the landmarks assigned to each 3D image. This method automatically generated a homogeneous model that consisted of 6,017 points (i.e., tted mesh or semi-landmark nodes) on the wire mesh for each model with landmark anchors (i.e., Ex, En, Ps, Pi, Prn, Ac, Sn, Ls, Sto, Ch, Li, Sm, and Pog).…”

Section: Analysesmentioning

confidence: 99%

Cross-racial Consistency of Sexual Dimorphism in Facial Forms: Comparisons Between Turkish and Japanese Populations

Tanikawa¹,

Mo²,

Gökalp³

et al. 2021

Preprint

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Facial features may reflect an individual’s probability of survival because of natural selection—for example, through reproduction and feeding abilities. However, questions remain about which facial characteristics contribute to facial masculinity and/or femininity across races. Given that sexual dimorphism stems from the common natural selection process, it is hypothesized that there is cross-racial consistency in sexual dimorphism of facial forms. Examining the extent to which sex differences in facial soft tissue configurations are similar across diverse populations could suggest the source of the indirect evolutionary benefits of facial sexual dimorphism traits. To explore this idea, we selected two geographically distinct races. Three-dimensional model faces were derived from 272 Turkish and Japanese men and women; their facial morphologies were evaluated using landmark and surface-based analyses. We found four common facial features related to sexual dimorphism. Both Turkish and Japanese females had a shorter lower face height, a flatter forehead, greater sagittal cheek protrusion in the infraorbital region but less prominence of the cheek in the parotid-masseteric region, and an antero-posteriorly smaller nose when compared with their male counterparts. We also discussed the possible phylogenetic contribution of masticatory organ function and morphogenesis on sexual dimorphism of the human face.

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Test-retest reliability of smile tasks using three-dimensional facial topography

Abstract: The overall test-retest reliability of the maximum effort smile and rest posture showed substantial to almost perfect agreement, and this was clinically acceptable.

Cited by 16 publications

References 21 publications

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Functional decline in facial expression generation in older women: A cross-sectional study using three-dimensional morphometry

Cross-racial Consistency of Sexual Dimorphism in Facial Forms: Comparisons Between Turkish and Japanese Populations

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