References
Jepson C et al (2005) In a mailed physician survey, questionnaire length had a threshold effect on response rate. J Clin Epidemiol 58(1):103–105
Chen K et al (2020) Facial recognition neural networks confirm success of facial feminization surgery. Plast Reconstr Surg 145(1):203–209
Dorfman R et al (2020) Making the subjective objective: machine learning and rhinoplasty. Aesthet Surg J 40(5):493–498
Jarvis T et al (2020) Artificial intelligence in plastic surgery: current applications, future directions, and ethical implications. Plast Reconstr Surg Glob Open 8(10):e3200
Patcas R et al (2019) Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod 41(4):428–433
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Peck, C.J., Patel, V.K., Parsaei, Y. et al. Commercial Artificial Intelligence Software as a Tool for Assessing Facial Attractiveness: A Proof-of-Concept Study in an Orthognathic Surgery Cohort. Aesth Plast Surg 46, 1013–1016 (2022). https://doi.org/10.1007/s00266-021-02537-4
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DOI: https://doi.org/10.1007/s00266-021-02537-4