Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups.


Journal

European journal of orthodontics
ISSN: 1460-2210
Titre abrégé: Eur J Orthod
Pays: England
ID NLM: 7909010

Informations de publication

Date de publication:
08 Aug 2019
Historique:
pubmed: 23 2 2019
medline: 25 9 2020
entrez: 22 2 2019
Statut: ppublish

Résumé

To evaluate facial attractiveness of treated cleft patients and controls by artificial intelligence (AI) and to compare these results with panel ratings performed by laypeople, orthodontists, and oral surgeons. Frontal and profile images of 20 treated left-sided cleft patients (10 males, mean age: 20.5 years) and 10 controls (5 males, mean age: 22.1 years) were evaluated for facial attractiveness with dedicated convolutional neural networks trained on >17 million ratings for attractiveness and compared to the assessments of 15 laypeople, 14 orthodontists, and 10 oral surgeons performed on a visual analogue scale (n = 2323 scorings). AI evaluation of cleft patients (mean score: 4.75 ± 1.27) was comparable to human ratings (laypeople: 4.24 ± 0.81, orthodontists: 4.82 ± 0.94, oral surgeons: 4.74 ± 0.83) and was not statistically different (all Ps ≥ 0.19). Facial attractiveness of controls was rated significantly higher by humans than AI (all Ps ≤ 0.02), which yielded lower scores than in cleft subjects. Variance was considerably large in all human rating groups when considering cases separately, and especially accentuated in the assessment of cleft patients (coefficient of variance-laypeople: 38.73 ± 9.64, orthodontists: 32.56 ± 8.21, oral surgeons: 42.19 ± 9.80). AI-based results were comparable with the average scores of cleft patients seen in all three rating groups (with especially strong agreement to both professional panels) but overall lower for control cases. The variance observed in panel ratings revealed a large imprecision based on a problematic absence of unity. Current panel-based evaluations of facial attractiveness suffer from dispersion-related issues and remain practically unavailable for patients. AI could become a helpful tool to describe facial attractiveness, but the present results indicate that important adjustments are needed on AI models, to improve the interpretation of the impact of cleft features on facial attractiveness.

Identifiants

pubmed: 30788496
pii: 5353220
doi: 10.1093/ejo/cjz007
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

428-433

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Raphael Patcas (R)

Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Switzerland.

Radu Timofte (R)

Computer Vision Laboratory, D-ITET, ETH Zurich, Switzerland.

Anna Volokitin (A)

Computer Vision Laboratory, D-ITET, ETH Zurich, Switzerland.

Eirikur Agustsson (E)

Computer Vision Laboratory, D-ITET, ETH Zurich, Switzerland.

Theodore Eliades (T)

Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Switzerland.

Martina Eichenberger (M)

Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Switzerland.

Michael Marc Bornstein (MM)

Oral and Maxillofacial Radiology, Applied Oral Sciences, Faculty of Dentistry, The University of Hong Kong, Prince Philip Dental Hospital, Hong Kong SAR, China.

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