Grant number: | 24/08695-2 |
Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
Start date until: | October 01, 2024 |
End date until: | September 30, 2025 |
Field of knowledge: | Health Sciences - Medicine - Surgery |
Principal Investigator: | José da Conceição Carvalho Júnior |
Grantee: | Vinicius Santos Baptista |
Host Institution: | Escola Paulista de Medicina (EPM). Universidade Federal de São Paulo (UNIFESP). Campus São Paulo. São Paulo , SP, Brazil |
Abstract IntroductionRhytidoplasty, commonly sought for facial rejuvenation, has evolved since the early 20th century with various techniques, including plication, high SMAS, and deep plane. Less invasive techniques promise lower complication rates and faster recovery, while more invasive methods claim long-lasting results and higher patient satisfaction. Studies indicate that patients generally feel satisfied with the improvement in appearance and rejuvenation post-surgery, with evaluations by surgeons and public perception corroborating these results. However, there is a gap in the literature regarding objective comparisons between different rhytidoplasty techniques in terms of attractiveness, perceived age, health, and success. Recent advancements in artificial intelligence enable precise analyses of facial attributes such as age, gender, and emotion, aiding in the comparison of techniques.ObjectivesTo compare, based on a randomized clinical trial, three rhytidoplasty techniques (deep plane, high SMAS, and plication) through the analysis of photographs taken before and after the procedure.MethodsThe study will be a randomized clinical trial. The research will be approved by the Ethics and Research Committee of UNIFESP, with informed consent obtained from patients and evaluators, ensuring anonymity and clarifying objectives, risks, benefits, and procedures. The population will include women between 45 and 60 years old undergoing rhytidoplasty (deep plane, high SMAS, or plication) from the Facial Outpatient Clinic at UNIFESP. Between October 2024 and February 2025, 15 patients will be selected, equally divided among the three techniques. Inclusion criteria involve voluntary women with complaints of facial sagging, while non-inclusion criteria encompass a history of recent facial surgeries, uncontrolled diseases, smoking, use of certain medications, and illiteracy. Demographic data will be collected in pre-operative consultations, and standardized photographs will be taken before and six months after surgery. Voluntary lay evaluators will be recruited to analyze the photos. Two online forms will be used: one with pre- and post-operative photos, and another with only post-operative photos. Evaluations will include estimates of age and ratings for attractiveness, perceived success, and health. Three AIs (Apple Vision Framework, Microsoft Azure Face API, and Amazon Rekognition) will be used to estimate perceived age before and after the procedures, with statistical comparisons between the AI estimates and human evaluators' assessments. | |
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