Perceived Age and Attractiveness Using Facial Recognition Software in Rhinoplasty Patients: A Proof-of-Concept Study.

Publication/Presentation Date

7-1-2022

Abstract

Artificial intelligence (Al)-based analyses may serve as a more objective tool for measuring cosmetic improvements following aesthetic plastic surgery. This preliminary proof-of-concept study utilized a novel commercial facial recognition software to assess perceived changes in age and attractiveness among patients receiving rhinoplasty.This study was a retrospective evaluation of three-dimensional photographs of patients who underwent rhinoplasty by the senior author (DS). Both pre- and post-operative (> 12-month follow-up) Vectra three-dimensional images (Canfield Scientific, Parsippany, NJ) were assessed using Haystack AI Software (Haystack AI, New York, NY). Facial attractiveness (score 1-10) and apparent age were predicted. A retrospective chart review of demographic variables was additionally performed. Paired t tests were used to compare age and attractiveness scores before and after surgery. Multivariate linear regression was performed to identify factors associated with age and attractiveness scores.One hundred twenty-four patients receiving rhinoplasty met the study criteria (average age: 35.58). Overall, rhinoplasty was associated with increases in Al-rated attractiveness (+0.28, P = 0.03) and decreases in perceived age relative to the patient's true age (-1.03 years, P = 0.03). Greater decreases in postoperative perceived age were achieved in patients who appeared older than their actual age preoperatively ( P < 0.001).Facial recognition software was successfully used to evaluate improvements in perceived age and attractiveness in patients undergoing aesthetic rhinoplasty. Patients were perceived by the software as younger and more attractive following rhinoplasty. Age reversal was greatest among patients who appeared much older than their actual age at the time of surgery.Level of Evidence: IV.

Volume

33

Issue

5

First Page

1540

Last Page

1544

ISSN

1536-3732

Disciplines

Medicine and Health Sciences

PubMedID

https://pubmed.ncbi.nlm.nih.gov/35288497/

Department(s)

Department of Surgery, Fellows and Residents, Department of Surgery Residents

Document Type

Article

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