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Age Reduction After Face-Lift Surgery in Chinese Population: An Outcome Study Using Artificial Intelligence and
Hong Du1, Haojun Liang1, Baoyun Peng2
1Plastic Surgery Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, #33 Badachu Road, Shijingshan District, Beijing, 100144, China.
Background:
The literature is replete with favorable face-lift results, yet the objective facial rejuvenation outcome measures in Chinese women have remained poorly understood.
Objective:
The purpose of the study is to objectively evaluate the apparent age (AA) reduction in Chinese women following face-lift by artificial intelligence (AI) and objective observers.
Methods:
Standardized pre- and postoperative (1-year) images of 48 patients undergoing face-lift procedures were analyzed by AI to estimate AA. Additionally, 10 blinded, naive observers viewed each patient's images and assessed AA. The accuracy of AA and reduction in AA were evaluated and compared between the two methods. FACE-Q surveys were employed to measure patient-reported facial esthetic outcomes.
Results:
The AI demonstrated higher precision than the observers in age estimation, with a mean absolute error of 3.34 years and 90% Pearson correlation. AA reduction generated by AI was significantly lower than that by observers, with a mean reduction of 3.75 ± 3.93 and 4.51 ± 1.20, respectively (p < 0.05). However, both methods showed less AA reduction than patient self-appraisal (- 7.3 years). Improvements in facial rejuvenation following face-lift surgery is relevant to the patient's preoperative aging status. Patients whose pre-AA was greater than chronological age (CA) became "back to normal," while those whose pre-AA was less than CA became "turning back the clock."
Conclusion:
The utilization of AI could provide objective, evidence-based data in the field of face-lift surgery. As a simple, complete, and time-sparing method, AI is expected to be routinely used in clinical trials and practice.
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 .
