Facial rhytidosis
Conditions
Interventions
Sponsors
Eligibility
Inclusion criteria
Inclusion criteria: Female patients; aged between 45 and 60 years; voluntary attendance at the Plastic Surgery Clinic at Universidade Federal de São Paulo – Escola Paulista de Medicina; complaints of rhytidosis and facial laxity
Exclusion criteria
Exclusion criteria: Patients who have undergone facial surgeries, implants, malar fillers, or chemical or physical peels in the past 12 months; diagnosis of uncontrolled acute or chronic diseases such as diabetes mellitus, systemic arterial hypertension, healing disorders, autoimmune diseases, collagen diseases, infectious diseases, and psychiatric disorders; smokers; use of anticoagulants, antiplatelet agents, or corticosteroids; history of hypersensitivity to anesthetics; illiteracy; abandonment of clinical follow-up at any stage of the research or voluntary withdrawal from participation during the study; undergoing any other aesthetic facial procedure during the 6-month follow-up, such as Botox injections, facial fillers with hyaluronic acid, chemical peels, microneedling, fractional lasers, non-surgical facelifts, radiofrequency treatments, and CO2 resurfacing procedures
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To assess the perceived age of patients before and after undergoing rhytidoplasty using the plication, High SMAS, and Deep Plane techniques; measured by the average age estimates from volunteer evaluators and artificial intelligence systems (Apple Vision Framework, Microsoft Azure Face API, and Amazon Rekognition). | — |
Secondary
| Measure | Time frame |
|---|---|
| To evaluate the perceived attractiveness, success, and health of patients after rhytidoplasty; analyzed using 0 to 100 scales assigned by volunteer evaluators in specific questionnaires;;To compare the difference in perceived age when evaluators analyze pre- and postoperative photographs together versus exclusive postoperative analysis, using online questionnaires as the data collection method;;To observe the consistency between perceived age estimates from artificial intelligence systems and human evaluators, using the intraclass correlation coefficient (ICC) to measure agreement. | — |
Countries
Brazil
Contacts
Universidade Federal de São Paulo