ArticleAesthetic plastic surgery2026
Is Asymmetry the New Perfect? Redefining Beauty with Artificial Intelligence.
Article in Aesthetic plastic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- The Relationship Between Dentofacial and Body Postural Asymmetries in Patients with Malocclusions-A Cross-Sectional Clinical Study.Medicina (Kaunas, Lithuania) · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis study challenges the traditional dominance of facial symmetry as the gold standard of beauty by exploring facial asymmetry's role in contemporary aesthetics. While symmetry has historically been associated with genetic fitness and attractiveness, evolving societal standards suggest growing recognition of asymmetry as a distinct aesthetic feature. This research aims to investigate whether facial asymmetry can play a positive role in beauty perceptions, especially across different demographic groups, and examines the influence of cultural norms, social media trends, and generational shifts.
methodUsing a dataset of 10,000 facial images, both real-world and synthetic, rated by 200 participants, this study applied machine learning models to quantify facial asymmetry and its relationship to attractiveness. Dlib and OpenCV were used to detect facial landmarks, measuring deviations in the jawline, mouth, and eyes. Multiple regression models and convolutional neural networks (CNNs) were employed to predict attractiveness, incorporating sociocultural variables like cultural norms, social media influence, and generational shifts.
resultsA moderate negative correlation (- 0.31) between facial asymmetry and attractiveness, with stronger sensitivity to jawline asymmetry was found. However, younger generations; Lesbian, Gay, Bisexual, Transgender, Queer/Questioning, Intersex, Asexual, including other diverse sexual and gender identities (LGBTQIA+) participants, and respondents from East Asia had more neutral or positive perceptions of asymmetry. CNN models achieved an R-squared value of 0.68, underscoring the impact of sociocultural factors. A quantum-like mathematical model further illustrated how beauty perceptions shift over time.
conclusionThis study highlights a shift in beauty standards, where asymmetry is gaining recognition, especially among younger generations and underrepresented groups. The findings suggest that the true essence of beauty lies in embracing individuality and redefining attractiveness beyond traditional ideals. 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 .
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Identifiers
41366109What 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.