Background Facial thread lifting is a minimally invasive procedure increasingly preferred for facial rejuvenation and attractiveness enhancement. Despite its popularity, objective assessments of its outcomes remain limited. Objective This study aimed to evaluate the effects of facial thread lifting on perceived facial age and attractiveness in women using artificial intelligence (AI) algorithms and human assessments. Methods A retrospective analysis was performed on 150 pre- and postoperative (2nd week and 6th month) photographs from 50 female patients treated with Silhouette threads. Photographs were evaluated using AI models trained for age prediction and attractiveness rating, as well as 20 blinded human evaluators (10 experts, 10 non-experts). Patient satisfaction was assessed through standardized surveys. Statistical analyses included repeated measures ANOVA, paired t-tests, and regression analysis. Results AI detected rejuvenation in 82% of patients with a mean reduction of 2.75 ± 0.47 years from baseline (T0) to 6 months (T2) (p < 0.0001) and increased attractiveness in 74% of patients with a mean score increase of 0.75 ± 1.33 (p < 0.0001). Human evaluators confirmed these findings, reporting a mean reduction in apparent age of 2.46 ± 0.45 years and an attractiveness score increase of 0.60 ± 0.11 (both p < 0.0001). No significant difference was observed between AI and human assessments (p > 0.05). Older patients experienced greater improvements in attractiveness (p = 0.02), while rejuvenation was independent of age (p = 0.31). Patient satisfaction was high, with 60% rating outcomes 5/5, correlating significantly with rejuvenation (p = 0.034) but not attractiveness (p = 0.082). Conclusions Facial thread lifting was associated with measurable reductions in estimated apparent age and modest improvements in attractiveness scores, with broadly consistent findings between AI and human evaluations. Patient satisfaction is primarily associated with perceived rejuvenation. AI-based assessments may offer a more standardized and reproducible approach for comparative evaluation, supporting their potential role in evidence-based aesthetic research.

Artificial Intelligence–Based Assessment of the Impact of Thread Lifts on Perceived Age and Attractiveness in Women

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发表日期：

2026年3月5日

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Yunus Balel,
Ricardo Grillo

摘要

Background

Facial thread lifting is a minimally invasive procedure increasingly preferred for facial rejuvenation and attractiveness enhancement. Despite its popularity, objective assessments of its outcomes remain limited.

Objective

This study aimed to evaluate the effects of facial thread lifting on perceived facial age and attractiveness in women using artificial intelligence (AI) algorithms and human assessments.

Methods

A retrospective analysis was performed on 150 pre- and postoperative (2nd week and 6th month) photographs from 50 female patients treated with Silhouette threads. Photographs were evaluated using AI models trained for age prediction and attractiveness rating, as well as 20 blinded human evaluators (10 experts, 10 non-experts). Patient satisfaction was assessed through standardized surveys. Statistical analyses included repeated measures ANOVA, paired t-tests, and regression analysis.

Results

AI detected rejuvenation in 82% of patients with a mean reduction of 2.75 ± 0.47 years from baseline (T0) to 6 months (T2) (p < 0.0001) and increased attractiveness in 74% of patients with a mean score increase of 0.75 ± 1.33 (p < 0.0001). Human evaluators confirmed these findings, reporting a mean reduction in apparent age of 2.46 ± 0.45 years and an attractiveness score increase of 0.60 ± 0.11 (both p < 0.0001). No significant difference was observed between AI and human assessments (p > 0.05). Older patients experienced greater improvements in attractiveness (p = 0.02), while rejuvenation was independent of age (p = 0.31). Patient satisfaction was high, with 60% rating outcomes 5/5, correlating significantly with rejuvenation (p = 0.034) but not attractiveness (p = 0.082).

Conclusions

Facial thread lifting was associated with measurable reductions in estimated apparent age and modest improvements in attractiveness scores, with broadly consistent findings between AI and human evaluations. Patient satisfaction is primarily associated with perceived rejuvenation. AI-based assessments may offer a more standardized and reproducible approach for comparative evaluation, supporting their potential role in evidence-based aesthetic research.