Outcomes after combined facial aesthetic surgery (CFAS), defined as the simultaneous performance of two or more facial rejuvenation procedures in a single operative setting, are commonly evaluated using subjective tools with high interrater variability. This pilot study aimed to introduce an objective, automated pipeline for CFAS outcome assessment by integrating the CAARISMA®ARMM algorithm with Vectra® imaging. Ten female patients who underwent elective CFAS were retrospectively analyzed using standardized pre- and postoperative (3-month) frontal Vectra® H2 images processed through the CAARISMA®ARMM system. The artificial intelligence (AI) algorithm automatically generated Facial Youth Index (FYI), Facial Attractiveness Index (FAI), and Skin Quality Index (SQI) values, supplemented by detailed analyses of skin texture and wrinkle parameters. Postoperative scores improved significantly across all indices: FYI increased (Δ relative (rel) 2.1 ± 1.8%; p = 0.016), FAI improved (Δ rel 12.3 ± 13.3%; p = 0.008), and SQI rose (Δ rel 14.3 ± 8.9%; p = 0.001). SQI demonstrated the most consistent relative improvements (coefficient of variation 62.3%), with the largest gains in fine relief (Δ rel 56.2%), roughness (Δ rel 15.6%), and rough relief (Δ rel 13.0%). Wrinkle scores improved most in crowfeet (Δ rel 10.4%) and infraorbital (Δ rel 6.6%) regions. This study is the first to demonstrate clinical integration of an automated AI algorithm with a standard clinical camera system to objectively assess CFAS outcomes. This workflow minimizes manual input and observer bias, offering a scalable, reproducible framework for benchmarking in aesthetic surgery. Larger, more diverse studies are needed to broaden clinical adoption.

Die ästhetische Medizin befindet sich im Wandel hin zu objektiveren, algorithmus- und datenbasierten Entscheidungen, wobei die KI(künstliche Intelligenz)-gestützte Gesichtsanalyse eine zentrale Rolle einnimmt. Moderne Systeme wie CAARISMA™ (ICA Aesthetic Navigation GmbH, Frankfurt) erfassen das Gesicht ganzheitlich anhand anatomischer Landmarken, integrieren diese in die Analyse verschiedener ästhetischer Gesichtspunkte und übersetzen diese in leicht verständliche Scores wie Facial Aesthetic Index (FAI), Facial Youthfulness Index (FYI) und Skin Quality Index (SQI). Diese Indizes ermöglichen eine standardisierte und reproduzierbare ästhetische Diagnostik, frei von subjektiven behandlerspezifischen Einflüssen. Klinisch führen sie zu strukturierteren Beratungen, individualisierten Therapieplänen und einer verbesserten Kommunikation zwischen Arzt und Patient. Gleichzeitig kann eine datengestützte ästhetische Diagnostik und Therapieplanung mittels KI die Effizienz und Behandlungsqualität im Praxisalltag erhöhen und erlaubt langfristige Verlaufskontrollen. Herausforderungen an der Schnittstelle zwischen KI und Ästhetik bestehen in den Bereichen Bias, Transparenz und Datenschutz, deren Berücksichtigung Voraussetzung für den verantwortungsvollen Einsatz dieser Technologien ist.

Aesthetic medicine is undergoing a substantial shift toward more objective, algorithm- and data-driven decision-making, with artificial intelligence (AI)-supported facial analysis playing a pivotal role. Modern systems such as CAARISMA™ capture the face holistically using anatomical landmarks, integrate these into the evaluation of different aesthetic dimensions, and translate them into easily interpretable scores such as Facial Aesthetic Index (FAI), Facial Youthfulness Index (FYI), and the Skin Quality Index (SQI). These indices enable standardized and reproducible aesthetic diagnostics, free from subjective, practitioner-specific influences. Clinically, they lead to more structured consultations, individualized treatment plans, and improved communication between physician and patient. At the same time, AI-based, data-driven aesthetic diagnostics and treatment planning can enhance efficiency and treatment quality in daily practice and allow for long-term monitoring of outcomes. Challenges at the interface between AI and aesthetics lie in the areas of bias, transparency, and data protection, all of which must be addressed to ensure responsible use of these technologies.

BACKGROUND:Skin quality has a significant influence on aesthetic perception, yet its clinical evaluation remains subjective and inconsistent. Traditional assessments, such as visual grading and manual scoring, lack reproducibility and fail to capture subtle changes over time. AIMS:To explore how artificial intelligence (AI) can transform skin quality evaluation by introducing objective, data-driven metrics that enhance precision, reproducibility, and personalization in aesthetic medicine. METHODS:We conducted a narrative review of the literature on AI-based skin analysis tools and their role in quantifying key skin quality dimensions, including pigmentation, texture, elasticity, radiance, and erythema. Emphasis was placed on the use of standardized imaging, emergent perceptual categories (EPCs), and composite scoring systems designed to capture multidimensional aspects of skin quality. RESULTS:AI tools enable the objective quantification of skin quality through high-dimensional image analysis, thereby reducing interobserver variability and supporting consistent evaluation across time points and populations. These systems facilitate longitudinal monitoring, tailored interventions, and patient-clinician communication. By integrating individual demographics and environmental variables, AI fosters equitable and personalized care. Regulatory and ethical considerations, such as data privacy and algorithmic bias, must be addressed to ensure the responsible implementation of these tools. CONCLUSIONS:AI represents a paradigm shift in aesthetic dermatology, offering standardized and reproducible metrics for assessing and monitoring skin quality. When aligned with validated frameworks, such as the EPCs, AI supports improved treatment outcomes, patient satisfaction, and industry-wide standardization. Future progress depends on interdisciplinary collaboration, robust regulation, and inclusive data practices.

Background Face transplantation (FT) offers a reconstructive option for patients with severe facial disfigurements by restoring both function and appearance. Aesthetic outcomes, which are crucial to psychological well-being and social reintegration, have historically been evaluated subjectively. This study introduces the AI Research Metrics Model (CAARISMA ® ARMM), a machine learning-based medical device designed to objectively assess aesthetic outcomes in FT patients. Methods Overall, 14 FT patients were analyzed using CAARISMA ® ARMM, which evaluates 3 key aesthetic indices: the Facial Youthfulness Index (FYI), Facial Aesthetic Index (FAI), and Skin Quality Index (SQI). Preoperative, postoperative, and pre-trauma images were processed to assess improvements in facial aesthetics. Statistical analysis was performed to compare changes in these indices across the different time points. Results Postoperative scores for FYI, FAI, and SQI were significantly higher than preoperative scores (p < 0.0001), indicating substantial aesthetic improvements. No significant differences were found between postoperative and pre-trauma images, suggesting that FT can effectively restore a patient's pre-injury appearance. Aesthetic improvements were consistent across different age and gender groups, with no notable disparities in outcomes. Conclusion CAARISMA ® ARMM offers a reliable and objective framework for objectifying aesthetic outcomes following FT, allowing for more standardized assessments. This medical device can potentially improve patient-surgeon communication, enhance surgical planning, and serve as a benchmark for evaluating long-term aesthetic success in FT patients. Future research should focus on expanding CAARISMA ® ARMM's application to larger and more diverse patient populations.

Background: Facial palsy (FP) is a widespread condition affecting over 3 million people annually, with a complex etiology requiring tailored, multidisciplinary management. Despite advancements, there remains a lack of reliable, automated tools for objective pre- and postoperative assessment, limiting progress in treatment optimization. This study introduces the AI Research Metrics Model (CAARISMA (R) ARMM) to evaluate FP severity and outcomes following microsurgical gracilis muscle transfer. Methods: We analyzed pre- and postoperative images of 20 FP patients using CAARISMA (R) ARMM, which identifies 17 facial landmarks and evaluates 1,030 parameters. CAARISMA (R) ARMM calculates three indices: Facial Youthfulness Index (FYI), Facial Aesthetic Index (FAI), and Skin Quality Index (SQI). All surgical procedures were performed by the senior author. Statistical analysis compared preoperative and postoperative scores using independent t-tests and Wilcoxon-Mann-Whitney tests, with significance set at p < 0.05. Results: Significant improvements were observed in the FAI scores post-surgery (p < 0.001). In contrast, FYI and SQI scores did not show significant postoperative changes (p = 0.39 and p = 0.60, respectively). Significant gender differences emerged: females showed increased FYI scores postoperatively, while males exhibited a decline (p = 0.0065). Age-related variations were also significant, with younger patients showing improved SQI and older patients experiencing declines (p = 0.040). Conclusion: The CAARISMA (R) ARMM effectively captures aesthetic improvements post-reanimation. Gender and age significantly influence outcomes, underscoring the key role of personalized and adaptable assessment tools. Future studies should integrate dynamic assessments and validate the CAARISMA (R) ARMM across additional patient populations. CAARISMA (R) ARMM holds promise as a standardized tool in FP outcome evaluation. (c) 2024 The Authors. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

BACKGROUND:Aesthetic medicine has traditionally relied on clinical scales for the objective assessment of baseline appearance and treatment outcomes. However, the scales focus on limited aesthetic areas mostly and subjective interpretation inherent in these scales can lead to variability, which undermines standardization efforts. OBJECTIVE:The consensus meeting aimed to establish guidelines for AI application in aesthetic medicine. MATERIALS AND METHODS:In February 2024, the AI Consensus Group, comprising international experts in various specialties, convened to deliberate on AI in aesthetic medicine. The methodology included a pre-consensus survey and an iterative consensus process during the meeting. RESULTS:AI's implementation in Aesthetic Medicine has achieved full consensus for enhancing patient assessment and consultation, ensuring standardized care. AI's role in preventing overcorrection is recognized, alongside the need for validated objective facial assessments. Emphasis is placed on comprehensive facial aesthetic evaluations using indices such as the Facial Aesthetic Index (FAI), Facial Youth Index (FYI), and Skin Quality Index (SQI). These evaluations are to be gender-specific and exclude makeup-covered skin at baseline. Age and gender, as well as patients' ancestral roots, are to be considered integral to the AI assessment process, underlining the move towards personalized, precise treatments. CONCLUSION:The consensus meeting established that AI will significantly improve aesthetic medicine by standardizing patient assessments and consultations, with a strong endorsement for preventing overcorrection and advocating for validated, objective facial assessments. Utilizing indices such as the FAI, FYI, and SQI allows for gender-specific, age adjusted evaluations and insists on a makeup-free baseline for accuracy.

Background: The use of validated scales is still considered the gold standard for evaluating the severity of an aesthetic facial condition. Objectives: The aim of this investigation was to create and validate 5-point photonumeric scales for the assessment of perioral lines and marionette lines. Methods: A medical team created 2 different novel 5-point photonumeric scales for the assessment of perioral lines and marionette lines. Eleven international raters were involved in the digital validation, and 4 raters performed a live validation. Results: For the Croma Static Perioral Lines-Assessment Scale, the digital interrater intraclass correlation coefficients (ICCs) were 0.88 (95% CI, 0.85-0.91) in the first rating and 0.87 (95% CI, 0.83-0.90) in the second rating. The digital intrarater ICCs were 0.90 (95% CI, 0.87-0.92). In the live rating, the interrater ICCs were 0.89 (95% CI, 0.85-0.93) in the first rating and 0.91 (95% CI, 0.87-0.93) in the second rating with an intrarater ICC of 0.91 (95% CI, 0.88-0.95). For the Croma Marionette Lines-Assessment Scale, the digital rating interrater ICCs were 0.85 (95% CI, 0.81-0.89) in the first rating and 0.87 (95% CI, 0.84-0.90) in the second rating with an intrarater ICC of 0.89 (95% CI, 0.88-0.91). In the live rating, the interrater ICCs were 0.73 (95% CI, 0.54-0.83) in the first rating and 0.79 (95% CI, 0.65-0.87) in the second rating with an intrarater ICC of 0.88 (95% CI, 0.83-0.94). Conclusions: The Croma Static Perioral Lines-Assessment Scale and the Croma Marionette Lines-Assessment Scale have exceptional inter- and intrarater agreements that justify their use in clinical and study settings for all ethnic groups.

Background: The objective of this investigation was to create and validate five-point photonumeric scales which assess static and dynamic forehead lines. Methods: Two different novel five-point photonumeric scales for the assessment of static and dynamic forehead lines were developed. Moreover, a photoguide was created, including subjects from both sexes, all age groups, and different Fitzpatrick skin types. A total of 11 raters from all over the world were involved in the digital validation, whereas four raters performed a live validation. Results: The Croma Static Forehead Lines–Assessment Scale showed almost perfect inter and intra-rater agreement in both the digital and the live setting with inter-rater intraclass correlation coefficients of 0.86 [95% confidence interval (CI): 0.82–0.89] in the first digital rating and 0.82 [95% CI: 0.78–0.86] in the second digital rating. The Croma Dynamic Forehead Lines–Assessment Scale showed almost perfect inter and intra-rater agreement in the digital setting with inter-rater intraclass correlation coefficients of 0.83 [95% CI: 0.79–0.86] in the first digital rating and 0.80 [95% CI: 0.75–0.84] in the second rating and almost substantial agreement in the live setting. Conclusions: The Croma Static Forehead Lines–Assessment Scale and the Croma Dynamic Forehead Lines–Assessment Scale have excellent inter and intra-rater agreements to be justifiably used in the clinical and study setting, both digitally and live across ethnic groups.

Background There is a scarcity of scales that assess platysmal bands, wrinkles in the décolleté, and horizontal neck lines in the digital and live setting. Objectives The objective of this investigation was to create and validate 5-point photonumeric scales that assess horizontal neck lines, platysmal bands, and wrinkles in the décolleté. Methods A medical team created 3 different novel 5-point photonumeric scales for the assessment of horizontal neck lines, platysmal bands, and décolleté wrinkling. Eleven international raters were involved in the digital validation, and 4 raters performed a live validation. Results The Croma (Leobendorf, Austria) Horizontal Neck Lines Assessment Scale showed substantial interrater agreement and almost perfect intrarater agreement in the digital and live validations, respectively. The Croma Platysmal Bands Assessment Scale showed substantial intrarater agreement in both digital and live validations. For the décolleté, a static scale and a dynamic scale were created and validated. The Croma Static Décolleté Wrinkles Assessment Scale showed substantial and almost perfect interrater agreement in the digital and live validations, respectively, and the intrarater agreement in both was almost perfect. The Croma Dynamic Décolleté Wrinkles Assessment Scale showed almost perfect agreement in both validation settings for both interrater and intrarater measures. Conclusions The Croma Horizontal Neck Lines Assessment Scale and the Croma Static and Dynamic Décolleté Wrinkles Assessment Scales have sufficient interrater and intrarater agreement for justifiable use in clinical and research settings.

Objective Quantifying the degree of dorsal hand atrophy is a challenging endeavor, but often necessary, in both the clinical and the research setting. The aim of this investigation was to create and consecutively validate a 5-point photonumeric scale for assessment of dorsal hand atrophy. Material and Methods A medical team created a novel 5-point photonumeric scale. Twelve international raters were involved in the digital validation, while five raters performed a live validation. Results For the digital validation of the Croma Hand Atrophy Assessment Scale, a total of 72 subjects (58 females, 14 males) with a mean age of 43.0 +/- 14.4 years [18-73 years] were assessed. For the live validation, 88 subjects (73 females, 15 males) with a mean age of 45.0 +/- 14.1 years [20-73 years] were rated. The results revealed almost perfect intra-rater (ICC: 0.90 [95% CI: 0.88-0.92]) and inter-rater agreements (ICC: 0.85 [95% CI: 0.81-0.89] and 0.86 [95% CI:0.82-0.89]) in the digital validation and substantial intra-rater (ICC: 0.79 [95% CI: 0.75-0.82]) and inter-rater agreements (ICC: 0.75 [95% CI: 0.68-0.81] and ICC: 0.67 [95% CI: 0.54-0.77]) in the live validation. Conclusion The created scale to assess dorsal hand atrophy has been shown to provide substantial-to-almost perfect agreement in the digital and live validation cycles and reached comparable intra-rater and inter-rater agreement to already published and validated scales. It is expected that the created scale will help physicians and researchers in the assessment of hand atrophy in the clinical and research setting in the future.

Objective The objective of this investigation was to create and validate 5-point photonumeric scales for the assessment of dynamic crow's feet, static crow's feet, and infraorbital hollows. Material and methods Three novel 5-point photonumeric scales were created by a medical team. A total of 12 raters from all over the world performed a digital validation, and a total of 5 raters a live validation of the created scale. Results The statistical analysis revealed almost perfect intra-rater and inter-rater reliability in the digital validation of the scales for the assessment of static and dynamic crow's feet as well as infraorbital hollows. In the live validation, both crow's feet scales showed almost perfect intra-rater reliability, while the Croma Infraorbital Hollow Assessment Scale showed substantial intra-rater reliability. Inter-rater reliability was substantial for all three scales in the live validation. All three scales, the Croma Dynamic Crow's Feet Assessment Scale, Croma Static Crow's Feet Assessment Scale, and Croma Infraorbital Hollow Assessment Scale, were validated digitally and in a live setting. Conclusion The created scales to assess infraorbital hollowing, dynamic and static crow's feet have been shown to provide substantial to almost perfect agreement in the digital and live validation and can thus be considered as helpful tools in the clinical and research setting. While technical methods and appliances to assess the degrees of severity of age-dependent features are advancing, validated scales are of great importance due to their ease of use and, as shown by the validations, reliability, and reproducibility.

Journal of Cosmetic DermatologyVolume 21, Issue 11 p. 6526-6527 LETTERS TO THE EDITOR Aesthetic medicine—Quo Vadis? Sebastian Cotofana MD, PhD, Corresponding Author Sebastian Cotofana MD, PhD [email protected] orcid.org/0000-0001-7210-6566 Department of Clinical Anatomy, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA Correspondence Sebastian Cotofana MD, PhD, Department of Clinical Anatomy, Mayo Clinic College of Medicine and Science, Mayo Clinic, Stabile Building 9-38, 200 First Street, Rochester, MN 55905, USA. Email: [email protected]Search for more papers by this authorSonja Sattler MD, Sonja Sattler MD Rosenpark Clinic, Darmstadt, GermanySearch for more papers by this authorKonstantin Frank MD, Konstantin Frank MD orcid.org/0000-0001-6994-8877 Department for Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilian University Munich, Munich, GermanySearch for more papers by this authorClaudia Hernandez MD, Claudia Hernandez MD CH Dermatologia, Medellin, ColombiaSearch for more papers by this authorTatjana Pavicic MD, Tatjana Pavicic MD Private Practice for Dermatology & Aesthetics Dr. Tatjana Pavicic, Munich, GermanySearch for more papers by this authorJeremy B. Green MD, Jeremy B. Green MD Skin Associates of South Florida and Skin Research Institute, Coral Gables, Florida, USASearch for more papers by this authorMartina Kerscher MD, PhD, Martina Kerscher MD, PhD Division of Cosmetic Science, Department of Chemistry, University of Hamburg, Hamburg, GermanySearch for more papers by this authorPeter Peng MD, Peter Peng MD orcid.org/0000-0002-5381-7167 Department of Dermatology, Tri-Service General Hospital, National Defense Medical Center, Taipei, TaiwanSearch for more papers by this authorMichael Gold MD, Michael Gold MD orcid.org/0000-0002-5183-5433 Gold Skin Care Center, Tennessee Clinical Research Center, Nashville, Tennessee, USASearch for more papers by this authorRainer M. Pooth MD, PhD, Rainer M. Pooth MD, PhD Clinical Research & Development, ICA Aesthetic Navigation GmbH, Frankfurt, GermanySearch for more papers by this author Sebastian Cotofana MD, PhD, Corresponding Author Sebastian Cotofana MD, PhD [email protected] orcid.org/0000-0001-7210-6566 Department of Clinical Anatomy, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA Correspondence Sebastian Cotofana MD, PhD, Department of Clinical Anatomy, Mayo Clinic College of Medicine and Science, Mayo Clinic, Stabile Building 9-38, 200 First Street, Rochester, MN 55905, USA. Email: [email protected]Search for more papers by this authorSonja Sattler MD, Sonja Sattler MD Rosenpark Clinic, Darmstadt, GermanySearch for more papers by this authorKonstantin Frank MD, Konstantin Frank MD orcid.org/0000-0001-6994-8877 Department for Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilian University Munich, Munich, GermanySearch for more papers by this authorClaudia Hernandez MD, Claudia Hernandez MD CH Dermatologia, Medellin, ColombiaSearch for more papers by this authorTatjana Pavicic MD, Tatjana Pavicic MD Private Practice for Dermatology & Aesthetics Dr. Tatjana Pavicic, Munich, GermanySearch for more papers by this authorJeremy B. Green MD, Jeremy B. Green MD Skin Associates of South Florida and Skin Research Institute, Coral Gables, Florida, USASearch for more papers by this authorMartina Kerscher MD, PhD, Martina Kerscher MD, PhD Division of Cosmetic Science, Department of Chemistry, University of Hamburg, Hamburg, GermanySearch for more papers by this authorPeter Peng MD, Peter Peng MD orcid.org/0000-0002-5381-7167 Department of Dermatology, Tri-Service General Hospital, National Defense Medical Center, Taipei, TaiwanSearch for more papers by this authorMichael Gold MD, Michael Gold MD orcid.org/0000-0002-5183-5433 Gold Skin Care Center, Tennessee Clinical Research Center, Nashville, Tennessee, USASearch for more papers by this authorRainer M. Pooth MD, PhD, Rainer M. Pooth MD, PhD Clinical Research & Development, ICA Aesthetic Navigation GmbH, Frankfurt, GermanySearch for more papers by this author First published: 27 August 2022 https://doi.org/10.1111/jocd.15334Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume21, Issue11November 2022Pages 6526-6527 RelatedInformation

Objective The objective of this investigation was to create and to examine the reproducibility and validity of 5-point photonumeric assessment scales that allow objective assessment of chin retrusion and jawline sagging using a digital and a live validation. Material and methods Two new 5-point photonumeric scales created to assess chin projection and jawline sagging were validated by 12 experts in a digital validation and by 5 experts in a live validation setting. Intra-rater agreement and inter-rater agreement were assessed. Results For the digital validation, an almost perfect intra-rater (Kappa: 0.89 [95% CI: 0.86-0.91]) and almost perfect inter-rater agreement in both sessions (Kappa: 0.80 [95% CI: 0.74-0.86] and 0.80 [95% CI: 0.74-0.86]) was achieved for the Croma Chin Projection Assessment Scale, while intra-rater agreement (Kappa: 0.88 [95% CI: 0.85-0.91]) was almost perfect for the Croma Jawline Sagging Assessment Scale and inter-rater agreement being substantial in the first session (Kappa: 0.76 [95% CI: 0.71-0.81]) and almost perfect in the second session (Kappa: 0.81 [95%CI: 0.76-0.85]). For the live validation, intra-rater agreement was almost perfect for the Croma Chin Projection Assessment Scale (Kappa: 0.82 [95%CI: 0.74-0.90]) and the Croma Jawline Sagging Assessment Scale (Kappa: 0.83 [95%CI: 0.77-0.89]), while inter-rater agreement was substantial in both sessions for both scales. Conclusion The created chin and jawline photonumeric grading scales are valid and reliable tools for assessing chin projection and jawline sagging. The scales will be of value for standardized chin evaluation and quantifying outcomes in clinical research and daily practice.

BACKGROUND:As the number of different aesthetic treatments increase, numerous photonumeric assessment scales have been developed and validated to measure the effectiveness of these new treatments and techniques. Photonumeric rating scales have been developed to objectively assess improvements in anatomical areas; however, these have been based on the features of Caucasian patients.OBJECTIVE:To develop and validate a Chin Projection Scale for use in the female Asian patient population.METHODS AND MATERIALS:During 2 validation sessions, 13 raters assessed full frontal and lateral facial views of 50 Asian subjects and also estimated their age and the aesthetic treatment effort required for each subject. Chin projection was rated on a scale from 0 (optimal) to 4 (very severely receding).RESULTS:Inter-rater reliability was 0.80 (substantial) for Validation Session 1 and 0.83 (almost perfect) for Validation Session 2. The results for Estimated Age and Estimated Treatment Effort were essentially the same.CONCLUSION:This study demonstrated the validity of the first photonumeric assessment scale for assessing the appearance of the female Asian chin. This new scale will provide a standardized measure of chin projection for Asian patients in clinical practice and clinical research settings.

BACKGROUND:Clinical photonumeric scales have been developed and validated to objectively measure the effectiveness of aesthetic treatments in specific anatomical areas; however, these are based on the typical features of Caucasian patients. No clinical scale for Asian calf appearance currently exists.OBJECTIVE:To develop and validate a calf assessment scale for use in the female Asian patient population.METHODS AND MATERIALS:During 2 validation sessions, 13 raters assessed calf images of female Asian subjects (N = 35) viewed from behind with feet flat on the floor (at rest) and on tiptoes (dynamic). Images were rated from 0 (very slim, linear profile) to 4 (very severe convex profile).RESULTS:Inter-rater and intra-rater reliability were "substantial" (≥0.6, intraclass correlation coefficient [ICC] and weighted kappa) for the calf-at rest, calf-dynamic, and calf summary score. Reliability was "substantial" for calf-at rest and calf-dynamic (≥0.6, ICC and weighted kappa) and "almost perfect" (0.85) for the calf summary score. BMI and calf circumference were highly correlated with scale ratings, and calf circumference was a significant predictor.CONCLUSION:This new photonumeric assessment scale has value for assessing the female Asian calf, providing a standardized measure of calf appearance in clinical practice and clinical research settings.

BACKGROUND:New treatment methods for cellulite require globally accepted scales for aesthetic research and patient evaluation. OBJECTIVE:To develop a set of grading scales for objective assessment of cellulite dimples on female buttocks and thighs and assess their reliability and validity. MATERIALS AND METHODS:Two photonumeric grading scales were created and validated for dimples in the buttocks in female patients: Cellulite Dimples-At Rest, and Cellulite Dimples-Dynamic. Sixteen aesthetic experts rated photographs of 50 women in 2 validation sessions. Responses were analyzed to assess inter-rater and intra-rater reliability. RESULTS:Overall inter-rater reliability and intra-rater reliability were both "almost perfect" (≥0.81, intraclass correlation efficient and weighted kappa) for the At Rest scale. For the Dynamic scale, inter-rater reliability and intra-rater reliability were "substantial" (0.61-0.80). There was a high correlation between the cellulite scales and body mass index, age, weight, and skin laxity assessments. CONCLUSION:Consistent outcomes between raters and by individual raters at 2 time points confirm the reliability of the cellulite dimple grading scales for buttocks and thighs in female patients and suggest they will be a valuable tool for use in research and clinical practice.

BACKGROUND:As the number of aesthetic treatments has grown, so have the number of photonumeric assessment scales used to compare the effectiveness of these aesthetic treatments in specific anatomical areas; however, these are primarily based on Caucasian features.OBJECTIVE:To assess the validity of the first aesthetic scale for assessing the slope of the Asian forehead. A secondary objective was to correlate this scale with subject demographics and baseline characteristics.METHODS:During 2 validation sessions, 13 raters assessed full frontal and lateral facial images of female (n = 28; 56.0%) and male (n = 22; 44%) subjects. For each subject, the severity of forehead sloping was graded from 0 (convex forehead, optimal forehead volume) to 4 (concave forehead, very severe sloping). Raters also assessed the age of each subject and the estimated aesthetic treatment effort required to treat each subject.RESULTS:Inter-rater reliability was "substantial" with scores of 0.67 and 0.68 for the first and second validation sessions, indicating high reliability. BMI showed the highest correlation with the scale and was a significant predictor in the final regression model.CONCLUSION:This photonumeric assessment scale will be useful for assessing the slope of the Asian forehead in both clinical and research settings.