BACKGROUND: Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery. METHODS: Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system-ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery. In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction. RESULTS: The neural network preoperative age accuracy score demonstrated that all four neural networks were accurate in identifying ages (mean score, 100.8). Patient self-appraisal age reduction reported a greater age reduction than neural network age reduction after a face lift (-6.7 years versus -4.3 years). FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (75.1 ± 8.1), quality of life (82.4 ± 8.3), and satisfaction with outcome (79.0 ± 6.3). Finally, there was a positive correlation between neural network age reduction and patient satisfaction. CONCLUSION: Artificial intelligence algorithms can reliably estimate the reduction in apparent age after face-lift surgery; this estimated age reduction correlates with patient satisfaction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.
BACKGROUND:Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery. METHODS: Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system-ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery. In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction. RESULTS: The neural network preoperative age accuracy score demonstrated that all four neural networks were accurate in identifying ages (mean score, 100.8). Patient self-appraisal age reduction reported a greater age reduction than neural network age reduction after a face lift (-6.7 years versus -4.3 years). FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (75.1 ± 8.1), quality of life (82.4 ± 8.3), and satisfaction with outcome (79.0 ± 6.3). Finally, there was a positive correlation between neural network age reduction and patient satisfaction. CONCLUSION: Artificial intelligence algorithms can reliably estimate the reduction in apparent age after face-lift surgery; this estimated age reduction correlates with patient satisfaction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.