Assessment of facial soft-tissue profiles based on lateral photographs versus three-dimensional face scans

Fink, Martin; Hirschfelder, Ursula; Hirschinger, Veronika; Schmid, Matthias; Spitzl, C R; Detterbeck, Andreas; Hofmann, Elisabeth

doi:10.1007/s00056-016-0055-z

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…All data regarding study groups were blinded prior to measurements and statistical evaluation. Since children with FAS show a delayed developmental trajectory, we chose to include slightly (but not statistically significant) younger children as controls in order to optimize comparability (8,38,39). Thus, the included children were similar in terms of parameters such as body length or weight.…”

Section: Biasmentioning

confidence: 99%

Simplifying Diagnosis of Fetal Alcohol Syndrome Using Machine Learning Methods

et al. 2022

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IntroductionThe fetal alcohol spectrum disorder (FASD) is a complex and heterogeneous disorder, caused by gestational exposure to alcohol. Patients with fetal alcohol syndrome (FAS—most severe form of FASD) show abnormal facial features. The aim of our study was to use 3D- metric facial data of patients with FAS and identify machine learning methods, which could improve and objectify the diagnostic process.Material and MethodsFacial 3D scans of 30 children with FAS and 30 controls were analyzed. Skeletal, facial, dental and orthodontic parameters as collected in previous studies were used to evaluate their value for machine learning based diagnosis. Three machine learning methods, decision trees, support vector machine and k-nearest neighbors were tested with respect to their accuracy and clinical practicability.ResultsAll three of the above machine learning methods showed a high accuracy of 89.5%. The three predictors with the highest scores were: Midfacial length, palpebral fissure length of the right eye and nose breadth at sulcus nasi.ConclusionsWith the parameters right palpebral fissure length, midfacial length and nose breadth at sulcus nasi, machine learning was an efficient method for the objective and reliable detection of patients with FAS within our patient group. Of the three tested methods, decision trees would be the most helpful and easiest to apply method for everyday clinical and private practice.

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Section: Biasmentioning

confidence: 99%

Simplifying Diagnosis of Fetal Alcohol Syndrome Using Machine Learning Methods

et al. 2022

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“…Two-dimensional (2D) facial measurement strategies have been developed based on 2D facial records, which can aid in the diagnosis of soft tissue aesthetic problems to some extent. However, due to the restriction of the dimension of the 2D measurement, drawbacks such as inaccuracy, the inaccessibility of three-dimensional (3D) facial morphology, and problematic superimposition of the anatomical structures have been identified in a few studies [ 2 , 3 ]. These major drawbacks limit the development of 2D measurements in the field of orthodontics.…”

Section: Introductionmentioning

confidence: 99%

The accuracy of a three-dimensional face model reconstructing method based on conventional clinical two-dimensional photos

Mao

Tian

et al. 2022

BMC Oral Health

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Background This study aims to investigate the accuracy of a three-dimensional (3D) face reconstruction method based on conventional clinical two-dimensional (2D) photos. Methods Twenty-three patients were included, and Character Creator v3.2 software with the Headshot v1.0 plugin was used for 3D face model reconstruction. Various facial landmarks were finely adjusted manually to refine the models. After preprocessing and repositioning, 3D deviation analysis was performed. The accuracy of the landmarks in different dimensions was determined, and twelve facial soft tissue measurements were compared to validate the clinical potential of the method. Result The reconstructed 3D face models showed good facial morphology with fine texture. The average root mean square errors between face scan models and reconstructed models at perioral area (1.26 ± 0.24 mm, 95%CI: 1.15–1.37 mm) were significantly smaller than the entire facial area (1.77 ± 0.23 mm, 95%CI:1.67–1.88 mm), P < 0.01. The deviation of menton of soft tissue was significantly larger than pronasale (P < 0.01). The deviations of all landmarks in the Y-direction were significantly larger than those in the other 2 dimensions (Y > Z > X, P < 0.01). A significant difference (P < 0.05) of approximately 1.5 mm was found for facial height. Significant differences (P < 0.05) were also identified in the remaining 6 soft tissue measurements, with average deviations no greater than 0.5 mm (linear measurement) or 1.2° (angular measurements). Conclusion A 3D face modeling method based on 2D face photos was revealed and validated. The reconstruction accuracy of this method is clinically acceptable for orthodontic measurement purposes, but narrow clinical indications and labor-intensive operations remain problems.

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