Evidence map›Paper›PMID 41761106›Full record

ArticleBMC ophthalmology2026

Artificial intelligence-assisted quantitative evaluation of upper eyelid blepharoplasty outcomes in dermatochalasis: methodological validation and clinical correlation.

Aynur Er Bilir, Özgür Eroğul

Abstract readValidation Study
In one paragraph

Article in BMC ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Aynur Er BilirDepartment of Ophthalmology, Edirne Sultan 1. Murat State Hospital, Edirne, Turkey. draynurer@gmail.com.
Özgür EroğulFaculty of Medicine, Department of Ophthalmology, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsThis prospective study aimed to assess the functional and aesthetic outcomes of upper eyelid blepharoplasty in patients with dermatochalasis using a validated artificial intelligence (AI)–assisted morphometric analysis system and to determine its accuracy, reproducibility, and clinical applicability.

methodsTwenty‑five patients (50 eyelids) with age‑related dermatochalasis underwent standardized upper eyelid blepharoplasty between April and November 2024. High‑resolution periocular photographs were obtained preoperatively and at 2 weeks postoperatively. Morphometric parameters—including MRD‑1, PFH, TPS, and BFS—were quantified automatically using a hybrid MediaPipe–OpenCV pipeline. Bilateral symmetry was quantified using an Asymmetry Index. Changes between pre- and postoperative asymmetry indices were analyzed using the Wilcoxon signed-rank test. Synthetic images were used solely for calibration, while all outcome measurements were derived from real patient photos. Accuracy was validated through manual measurements by two ophthalmologists, yielding an intraclass correlation coefficient (ICC) of 0.93 (p < 0.001).

resultsAll patients completed follow‑up. The AI system demonstrated 97.5% accuracy and significant postoperative improvements across all parameters (p < 0.001) such as 86.7% precision, and a 92.9% F1-score in landmark detection, with a mean IoU of 0.82 and mAP > 0.80. Measurement variability decreased 68% versus manual methods. Area under the ROC curve (AUC) was 0.85 (95% CI 0.78–0.91), confirming strong discriminative power.

conclusionAI‑assisted morphometric analysis allows objective, reproducible, and quantitative evaluation of blepharoplasty outcomes, minimizing observer bias and establishing standardized benchmarks for future surgical audits.

Indexed as

Artificial IntelligenceBlepharoplastyEyelid DiseasesEyelidsAdultAgedFemaleHumansMaleMiddle AgedProspective StudiesReproducibility of ResultsTreatment OutcomeArtificial intelligenceBlepharoplastyComputer VisionDermatochalasisImage AnalysisOutcome Validation

Identifiers

PMID41761106
PMCPMC13049709

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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.