Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. (21st February 2019)

• Record Type: Journal Article
• Authors: Patcas, Raphael; Timofte, Radu; Volokitin, Anna; Agustsson, Eirikur; Eliades, Theodore; Eichenberger, Martina; Bornstein, Michael Marc
• Is Part Of: European journal of orthodontics. Volume 41:Number 4(2019)
• Page Start: 428
• Page End: 433
• Publication Date: 2019-02-21
• Subjects: Orthodontics -- Periodicals; 617.643
• DOI: 10.1093/ejo/cjz007
• Languages: English
• ISSNs: 0141-5387

Abstract

Summary: Objectives: 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. Materials and methods: 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). Results: 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 P s ≥ 0.19). Facial attractiveness of controls was rated significantly higher by humans than AI (all P s ≤ 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). Conclusions: 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. Implication: 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.