Disclosures

Did you receive any funding to support your research for this TOPIC?
No

Were you provided with any honoraria, payment or other compensation for your work on this study?
No

Do you have any financial relationship with any entity which may closely compete with the medications, materials or instruments covered by your study?
No

Do you own or have you applied for any patents in conjunction with the instruments, medications or materials discussed in your study?
No

This work was not supported by any direct or non direct funding. It is under the author's own responsability

Objectives: -Highlights the most significant studies tell 2025 updates as reference in your practice to guide you for properly interact with the concept of prrejuvenation .
-understand the new concept of prerejuvantion as a global new anti aging trend and what social media tell us about the rising trend.
-to know the cosmetic concern among generation Z among male and female ,what to do and what
not do .
-to be aware of the last industry trends for prrejuvenation .
discuss the ethical foundations for patient-centered care in aesthetic medicine for young generation

Objectives: This talk will focus on the evolution of facial and non-facial approaches with a focus on injectables, topical approaches and the use of energy based devices in these very different demographics.

Introduction: Although available treatments are similar in the Prejuvenation group of patients (Milennials and Gen Z -1980-2015) as compared to Traditional Rejuvenation patients ( Gen X and Baby Boomers 1944-1979), approaches and sources of information used by these groups are different...

Materials / method: This talk will focus on the evolution of facial and non-facial approaches with a focus on injectables, topical approaches and the use of energy based devices in these very different demographics.

Results: Some approaches for all groups are similar; others are not. Goals of different demographics may be different

Conclusion: Younger demographics are increasingly seeking treatment and different treatment approaches must be considered in this group.

Disclosures

Did you receive any funding to support your research for this TOPIC?
No

Were you provided with any honoraria, payment or other compensation for your work on this study?
No

Do you have any financial relationship with any entity which may closely compete with the medications, materials or instruments covered by your study?
No

Do you own or have you applied for any patents in conjunction with the instruments, medications or materials discussed in your study?
No

This work was not supported by any direct or non direct funding. It is under the author's own responsability

Objectives: CASE REPORT AND REVISION

Disclosures

Did you receive any funding to support your research for this TOPIC?
No

Were you provided with any honoraria, payment or other compensation for your work on this study?
No

Do you have any financial relationship with any entity which may closely compete with the medications, materials or instruments covered by your study?
No

Do you own or have you applied for any patents in conjunction with the instruments, medications or materials discussed in your study?
No

This work was not supported by any direct or non direct funding. It is under the author's own responsability

Disclosures

Did you receive any funding to support your research for this TOPIC?
No

Were you provided with any honoraria, payment or other compensation for your work on this study?
No

Do you have any financial relationship with any entity which may closely compete with the medications, materials or instruments covered by your study?
No

Do you own or have you applied for any patents in conjunction with the instruments, medications or materials discussed in your study?
No

This work was not supported by any direct or non direct funding. It is under the author's own responsability

Objectives: Integration of Multimodal Data combining clinical evaluations and instrumental measurements (e.g., LC-OCT, HFUS, Cutometer®, skinFlex®) enhances the accuracy of predictive models for clinical scores.

Significance of Variable Selection for identifying key parameters (VIPs) such as cellular metrics and collagen density is crucial for constructing effective predictive models.

Inclusive Research Approach incorporating diverse ethnic groups ensures predictive models are representative and applicable to a wide range of populations.

Introduction: Clinical evaluation of facial aging and instrumental measurements are complementary approaches. The goal is to propose a multivariate (chemometric) mathematical approach capable of constructing predictive models of clinical scores, establishing correlations, and explaining the appearance of clinical signs through modifications in the subsurface microstructures of the skin at structural, cellular, and biomechanical levels.

Materials / method: Data were collected from 300 healthy female volunteers (Caucasian, Asian, Afro-American). More than 200 quantitative features, including clinical scores and metrics derived from 3D LC-OCT imaging, HFUS, Cutometer®, and skinFlex®, were analyzed. To account for possible non-linearity, each original variable was transformed into four splines, then processed through Sparse-Partial Least Square for variable selection using phototypes as dummy variables. The performance of prediction was assessed through cross-validation using 50 iterations, each using different training and test sets.

Results: Predicting clinical scores using S-PLS yielded Root Mean Square Error Prediction between 1.4 and 1.8, underscoring the importance of integrating metrics from multiple techniques and the correlation between clinical and instrumental features. Variable Importance in the Projection identified key parameters. Cellular metrics from LC-OCT and collagen density from HFUS were as crucial as elasticity, tonicity, and firmness measured by Cutometer® and skinFlex®. The skinFlex®-LC-OCT block was predominant (73% of top 100 VIPs), while HFUS with biomechanical measurements represented 2%.

Conclusion: Predicting clinical scores using multivariate analysis (S-PLS) provides a unique perspective on the links between visible features of aging and instrumental measurements while the approach integrates the notion of ethnic groups for an inclusive study of facial aging mechanisms.

Disclosures

Did you receive any funding to support your research for this TOPIC?
No

Were you provided with any honoraria, payment or other compensation for your work on this study?
No

Do you have any financial relationship with any entity which may closely compete with the medications, materials or instruments covered by your study?
No

Do you own or have you applied for any patents in conjunction with the instruments, medications or materials discussed in your study?
No

This work was not supported by any direct or non direct funding. It is under the author's own responsability

Disclosures

Did you receive any funding to support your research for this TOPIC?
No

Were you provided with any honoraria, payment or other compensation for your work on this study?
No

Do you have any financial relationship with any entity which may closely compete with the medications, materials or instruments covered by your study?
No

Do you own or have you applied for any patents in conjunction with the instruments, medications or materials discussed in your study?
No

This work was not supported by any direct or non direct funding. It is under the author's own responsability