Facial Determinants of Artificial Intelligence-Perceived Gender and Age Following Facial Feminization Surgery
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View abstract on PubMed
Summary
This summary is machine-generated.Facial feminization surgery (FFS) significantly enhances perceived femininity and reduces perceived age, with specific facial thirds yielding greater improvements in younger versus older patients. AI analysis confirms these positive outcomes across all facial regions.
Area Of Science
- Plastic Surgery
- Artificial Intelligence
- Facial Analysis
Background
- Facial feminization surgery (FFS) aims to align facial features with female norms.
- Previous studies explored the impact of facial thirds on perceived femininity and youth.
- Objective quantification of FFS outcomes using AI is an emerging area.
Purpose Of The Study
- To objectively quantify the effects of FFS on perceived age and gender.
- To analyze the contribution of each facial third to overall femininity and youthfulness post-FFS.
- To compare AI-driven assessments of FFS outcomes across different age groups.
Main Methods
- Utilized frontal preoperative and postoperative images of 31 transgender women undergoing FFS.
- Created digital images isolating changes in the upper, middle, or lower facial thirds.
- Employed Amazon Rekognition AI to assess gender typing, femininity, and age scores by facial third.
Main Results
- Postoperative femininity scores improved significantly, particularly in the lower third (62.1%) and upper third (56.4%).
- Younger cohorts showed improved femininity in the lower face; older cohorts showed improvement in the upper face.
- AI perceived patients as significantly younger post-FFS, with reductions of 1.94-2.45 years across facial thirds.
Conclusions
- FFS effectively enhances perceived femininity, with age-dependent improvements in specific facial thirds.
- All facial thirds contribute to a significant reduction in perceived age following FFS.
- AI provides objective metrics for evaluating FFS efficacy in feminization and age perception.
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