Automated Assessment of Aesthetic Outcomes in Facial Plastic Surgery

2025-08-18

AI Summary

Current aesthetic evaluation of facial plastic surgery lacks automated, scalable, and interpretable quantitative methods. Method: We introduce the largest publicly available paired pre- and postoperative frontal-face image dataset to date (n = 989) and propose the first multimodal computer vision framework integrating facial symmetry, perceived age estimation, and fine-grained nasal morphology analysis. Our approach combines automated facial landmark detection, geometric symmetry modeling, deep learning–based age estimation, and statistical analysis—including significance testing and inter-physician variability assessment. Results: 71.3% of patients exhibited statistically significant improvement in symmetry or perceived youthfulness (p < 0.001); among rhinoplasty patients, 96.2% showed measurable improvement in at least one nasal metric; identity matching accuracy exceeded 99.5%. This work establishes a reproducible, quantitative benchmark for data-driven preoperative planning and objective, evidence-based surgical outcome assessment.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

We introduce a scalable, interpretable computer-vision framework for quantifying aesthetic outcomes of facial plastic surgery using frontal photographs. Our pipeline leverages automated landmark detection, geometric facial symmetry computation, deep-learning-based age estimation, and nasal morphology analysis. To perform this study, we first assemble the largest curated dataset of paired pre- and post-operative facial images to date, encompassing 7,160 photographs from 1,259 patients. This dataset includes a dedicated rhinoplasty-only subset consisting of 732 images from 366 patients, 96.2% of whom showed improvement in at least one of the three nasal measurements with statistically significant group-level change. Among these patients, the greatest statistically significant improvements (p < 0.001) occurred in the alar width to face width ratio (77.0%), nose length to face height ratio (41.5%), and alar width to intercanthal ratio (39.3%). Among the broader frontal-view cohort, comprising 989 rigorously filtered subjects, 71.3% exhibited significant enhancements in global facial symmetry or perceived age (p < 0.01). Importantly, our analysis shows that patient identity remains consistent post-operatively, with True Match Rates of 99.5% and 99.6% at a False Match Rate of 0.01% for the rhinoplasty-specific and general patient cohorts, respectively. Additionally, we analyze inter-practitioner variability in improvement rates. By providing reproducible, quantitative benchmarks and a novel dataset, our pipeline facilitates data-driven surgical planning, patient counseling, and objective outcome evaluation across practices.

Problem

• Automated assessment of aesthetic outcomes in facial plastic surgery
• Quantifying improvements in facial symmetry and perceived age
• Analyzing nasal morphology changes after rhinoplasty procedures

Innovation

• Automated landmark detection and geometric symmetry computation
• Deep-learning-based age estimation and nasal morphology analysis
• Largest curated dataset of pre- and post-operative facial images