Comparing the use of static versus dynamic images to evaluate a smile

Mahn, Eduardo; Sampaio, Camila S.; Silva, Bruno Pereira da; Stanley, Kyle; Valdes, Ana M.; Gutiérrez, Jaime; Coachman, Christian

doi:10.1016/j.prosdent.2019.02.023

Cited by 27 publications

(43 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are a few studies from different ethnic communities that show that the smile line may be found to be different in a patient’s comfortable position, since the smile line assessment is mostly based on the patient’s photographs and the patient is asked to pose ( 8 , 22 ). Also in this study, the prevalence of the smile line types changed between the posed smile and the spontaneous smile.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of gingival display during static and dynamic smiles in a Turkish sample: a clinical study

Arifağaoğlu¹,

Yilmaz²

2021

eor

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

“…For all those purposes, this static analysis can lead to misdiagnosis and non-ideal treatment because patients’ natural smiles may be significantly different from their posed smiles, displaying more teeth and/or gingiva. Dynamic smile assessments should be used to determine the entire range of a spontaneous smile ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of gingival display during static and dynamic smiles in a Turkish sample: a clinical study

Arifağaoğlu¹,

Yilmaz²

2021

eor

View full text Add to dashboard Cite

show abstract

“…Some previously published studies suggest that the use of photographs was not a reliable way to study smile attractiveness as this method could not capture the dynamic smile unlike the video recording method. 38 39 However, in the best interest of time, it was not possible to invite each of the 68 consecutive patients back to perform video recordings. As such, the investigators of the present study decided to use photographs instead with the knowledge of the compromise made.…”

Section: Limitationsmentioning

confidence: 99%

Smile Attractiveness Evaluation of Patients Selected for a U.S.-Based Board Certification Examination

Soh¹,

Wang

Zhang

et al. 2021

Eur J Dent

View full text Add to dashboard Cite

Objectives The aims of the study were to assess smile attractiveness of a collection of 68 smiling photographs of successfully treated cases submitted to the American Board of Orthodontics (ABO) clinical examination and identify variables that influence the assessment. Materials and Methods A panel of 81 non-Caucasian assessors from various clinical disciplines were instructed to score the smile attractiveness on a visual analog scale from 1 (least attractive) to 10 (most attractive) and to select which components contributed to a lesser attractive smile. The mean, standard deviations (SDs), and quartiles of the smile attractiveness were obtained with descriptive statistics. Multilinear regression analysis was performed to investigate the scores of the perceived quality of smile attractiveness when the clinical disciplines and gender of the assessors were the factors taken into consideration. Receiver operating characteristic (ROC) curve was generated to establish the relationship between smile attractiveness and the achievement of a perfect smile. Results The mean (SD) rating of each clinical photograph of the anterior occlusion on smiling ranged from 3.11 (1.47) as the least attractive smile to 7.59 (1.45) as the most attractive smile. The overall mean (SD) score for smile attractiveness was 5.30 (1.10). Problems associated with teeth, gingiva, and lips corresponded with a reduction of the smile attractiveness score by 1.56, 1.82, and 1.47, respectively. Gender was not associated with smile attractiveness ratings. Orthodontists, periodontists, and prosthodontists demonstrated no difference in the ratings, while plastic surgeons were more critical than orthodontists regarding smile attractiveness. Conclusions The study suggested that only 2 out of 68 AOB validated treatment finishes had a perfect and attractive smile.

show abstract

“…However, 2D DSD has a limitation in that it has to evaluate a patient with only one photograph taken in a still state, and the profile of the patient cannot be observed from various angles [13]. In addition, because the photographed smile is guided by a clinician, it might not be the natural smile of the patient, and it is difficult to photograph the ideal state [8,14]. A patient's smile cannot be precisely reproduced; moreover, because the degree of laughter can be very diverse, it is not possible to grasp in detail the various teeth, gingival exposure and smile characteristics that a patient could have.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Precision of Automatic Segmentation of Teeth, Gingiva and Facial Landmarks for 2D Digital Smile Design Using Real-Time Instance Segmentation Network

Lee

Kim

2022

JCM

View full text Add to dashboard Cite

Digital smile design (DSD) technology, which takes pictures of patients’ faces together with anterior dentition and uses them for prosthesis design, has been recently introduced. However, the limitation of DSD is that it evaluates a patient with only one photograph taken in a still state, and the patient’s profile cannot be observed from various viewpoints. Therefore, this study aims to segment the patient’s anterior teeth, gingiva and facial landmarks using YOLACT++. We trained YOLACT++ on the annotated data of the teeth, lips and gingiva from the Flickr-Faces-HQ (FFHQ) data. We evaluated that the model trained by 2D candid facial images for the detection and segmentation of smile characteristics. The results show the possibility of an automated smile characteristic identification system for the automatic and accurate quantitative assessment of a patient’s smile.

show abstract

Comparing the use of static versus dynamic images to evaluate a smile

Cited by 27 publications

References 17 publications

Analysis of gingival display during static and dynamic smiles in a Turkish sample: a clinical study

Analysis of gingival display during static and dynamic smiles in a Turkish sample: a clinical study

Smile Attractiveness Evaluation of Patients Selected for a U.S.-Based Board Certification Examination

Evaluating the Precision of Automatic Segmentation of Teeth, Gingiva and Facial Landmarks for 2D Digital Smile Design Using Real-Time Instance Segmentation Network

Contact Info

Product

Resources

About