Quantifying Facial Rejuvenation after Deep Plane Facelift Using Convolutional Neural Network-Based Age Estimation: Correlation with Patient-Reported Outcomes on the FACE-Q Aesthetic Module.

Abstract

Introduction: To quantify perceived age reduction following deep plane face and neck lift surgery using five publicly available CNN models and evaluate the correlation between AI-estimated rejuvenation and FACE-Q Aesthetic Module outcomes.

Methods: A retrospective cross-sectional study of 72 female patients undergoing deep plane face and neck lift surgery between 2020 and 2024 was conducted. Standardized photographs were analyzed using five CNN platforms: FacePlusPlus, Amazon Rekognition, Inferdo Face, howOld AI, and Facelytics. The arithmetic midpoint was used for the range-reporting models. A corrected and standardized age reduction metric was computed for each model and averaged into a composite score. The FACE-Q Aesthetic Module was administered at the 12-month follow-up. Spearman's rank correlation analysis was performed.

Results: The composite standardized age reduction was 7.64 ± 3.44 years (15.67 ± 6.76%). Inter-model differences were not significant (Friedman test: χ²(4) = 9.23, p = 0.056). Patient-perceived age reduction correlated strongly with facial appearance (ρ = 0.61, p < 0.001), outcomes (ρ = 0.54), and psychological well-being (ρ = 0.44). AI-estimated rejuvenation showed weaker associations, with only the early life impact reaching significance (ρ = 0.25, p = 0.034).

Discussion: CNN-based age estimation provides a reproducible and observer-independent metric for quantifying facial rejuvenation. AI-estimated and patient-perceived rejuvenation represent complementary dimensions of surgical outcomes, and their integration enables a more rigorous multidimensional evaluation framework.

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