ArticleAesthetic plastic surgery2025
AI-Driven Assessment of Lip Volume Improvement Using Hyaluronic Acid Fillers: A Comprehensive Analysis.
Article in Aesthetic plastic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technological Integration in Aesthetic Practice: A Systematic Review of Artificial Intelligence, Augmented Reality and Robotics in Cosmetic Procedures.Aesthetic plastic surgery · 2026Pooled it
- A Psychological Guide to Lower Face Botulinum Toxin Injections: Baseline Emotional Functions of Facial Expressions.Journal of cosmetic dermatology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe lips are a popular area for filler treatments across all age groups. Existing evaluations of lip volume enhancement often rely on subjective, visual assessments, and fail to account for the overall facial balance and esthetic changes. To address this, this study uses various AI models for a comprehensive analysis to better evaluate the effects of lip enhancement and provide insights for patient-specific treatment plans.
methodsThis study involved 31 participants (29 females, 2 males), all of whom received lip HA filler injections. Pre- and post-treatment facial images were captured in a relaxed state, and these images were processed using AI-based tools to record and analyze facial visual analysis, perceived age, and emotional expressions displayed on the face.
resultsPost-treatment, the facial visualization analysis revealed that as lip volume increased, the lip line became more defined and the natural curve of the lips became more balanced. The MiVOLO model demonstrated high age estimation accuracy (MAE: 5.15; ICC:0.866; Pearson's r: 0.875), with a mean apparent age reduction of - 1.63 ± 2.51 years (95% CI = -2.78 to - 0.49, Cohen's d = -0.65), emotion analysis revealed a significant increase in neutral expression (72.97-83.44%, 95% CI = 3.38 to 17.56, Cohen's d = 0.58) and reductions in anger (10.25-5.84%, 95% CI = -8.53 to - 2.28, Cohen's d = -0.42).
conclusionsThis study provides significant insights by showing that lip volume improvement affects not only external changes but also emotional expression, age perception, and social interaction. This approach presents a new solution in the field of cosmetic surgery and will contribute to improving patient satisfaction through personalized treatments and multifaceted effect analysis. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Indexed as
Identifiers
40374779What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.