Purpose

This meta-analysis aimed to provide an accurate assessment of the planning and aesthetic outcomes of blepharoplasty-related surgeries using artificial intelligence (AI).

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Methods

The methods followed the PRISMA guidelines. The literature search was conducted in PubMed, EMBASE, Scopus, Web of Science, and SciELO databases (November 2025 to February 2026). Randomised controlled clinical trials and other types of clinical studies (last 5 years) with a sample size greater than 30 participants were eligible (PICOS strategy). The quality of evidence (GRADE classification) and methodological quality (AMSTAR-2) were applied. The risk of bias was analysed using the Funnel-Plot. The Forest Plot graph was developed to present the effect of AI on the aesthetic results of blepharoplasty. The Chi-Square (χ2) test was used to assess heterogeneity among the analyses.

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Results

Twenty-four (24) important clinical studies were selected, of which 05 were randomised controlled trials (RCTs), 07 prospective controlled studies, and 12 retrospective observational studies, totaling 2,489 participants and 4,978 eyes. The analysed studies showed homogeneity in the results of the positive impact of AI on the aesthetic optimization of blepharoplasty surgery, with X2=88.95%. The Funnel-Plot showed a symmetrical behavior (no risk of bias). Binary logistic regression analysis showed significant improvement in the aesthetic results of blepharoplasty with AI in relation to the control group (without the use of AI), with OR=6.85 (CI: 3.34-8.35 and p<0.05).

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Conclusion

AI has the potential to provide objective assessments and optimizations of aesthetic outcomes. However, there are significant challenges in the analysis of non-standardized image data. Also, it is necessary to quantify the impact of blepharoplasty on age perception using convolutional neural network (CNN) algorithms. It was concluded that AI-assisted morphometric analysis allows for objective, reproducible, and quantitative evaluation of blepharoplasty outcomes, minimizing observer bias and establishing standardized parameters for future surgical audits.

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Additional Authors

First name	Last name	Base Hospital / Institution
Malu Ines	Perez Moura Henriques	Alphamed Clínica de Olhos (Alphamed Eye Clinic), Ayrton Senna da Silva Avenue, 500, room: 1201, Londrina, Paraná, Brazil.
Idiberto José	Zotarelli Filho	UNESP- São Paulo State University, São José do Rio Preto, Brazil, and ABRAN – Brazilian Association of Nutrology, Catanduva, São Paulo, Brazil.

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